The Sports Resource Blog

We Can Do That

2011-09-20T09:43:00.000-07:00

Critics like to point out the shortcomings of sports statistics. There are areas where the numbers have minimal impact. But much of the criticism comes from misunderstanding what analytics do best.

A recent article stated that statistics do a much better job of describing the past than predicting the future. While there is some truth to that point, the predictive power depends on what you’re measuring.

The article states that no stat could have predicted that Mark Whiten would have the greatest one-game offensive performance ever. That’s true, but it’s also an impossible task. On the other hand, metrics like batting average on balls in play and home runs per fly ball can identify which hitters should break out after slow starts. Remember Dan Uggla earlier this season?

The real power of sports analytics is what they can do now and will do in the future, especially when combined with technology and/or social media. Last week, I learned about an Austin startup which built an algorithm that measures the level of excitement in sports games. Now fans know immediately if their favorite team is headed for a thrilling finish and whether to watch the game or not.

Several years ago, while struggling to learn a new computer program, a colleague offered invaluable advice: write down everything you’d like the program to do, because it probably has that capability. Turns out that it could do everything I thought of and far more.

I believe analytics and technology can solve many of the problems agents and other sports insiders face. In general, sports analytics can do much more than we realize.

What is your greatest challenge? Analytics may have a solution.

Simple and Informative

2011-08-17T19:53:00.000-07:00

Save percentage is a useful statistic for closers, but not for other types of relievers. It has minimal value when evaluating setup men. Relievers that protect leads in the seventh and eighth innings rarely close games. Therefore, they have few chances to earn saves, but can still get blown saves.

That explains how an outstanding setup man like Mike Adams could have a 50 percent save percentage (two saves in four save opportunities). The stat fails to demonstrate his ability to maintain leads, which he had shown by accumulating 24 holds this season.

Save plus hold percentage evens the playing for closers and setup men, showing how well all relievers maintain leads. It is simple to calculate, yet gets little attention in the mainstream sports media. A hold is a save situation that gets preserved and passed on to the next reliever. So a hold is basically a save that does not end a game.

To calculate save plus hold percentage, combine saves and holds and then divide by saves, holds and blown saves. Among relievers with at least 15 save and hold opportunities through August 16, these pitchers led the Major Leagues.

The top 10 includes four closers, five setup men and one pitcher (Antonio Bastardo) who has filled both roles.

Adams’ 92.9 save plus hold percentage left him just short of the top 10. But he easily surpassed the Major League average of 84.9 percent this season.

Since it’s easy to explain and informative, save plus hold percentage makes a great tool for agents in both arbitration and free agency.

The Accelerators: Point Guards who Pick up the Pace

2011-08-15T12:56:00.000-07:00

When Pooh Jeter took the court for the Sacramento Kings last season, his teammates had to be ready to run. Based on a metric comparing team pace with and without each NBA player on the court, Jeter increased the pace more than any other point guard.

The Kings had 4.1 more possessions per 48 minutes when Jeter played versus when he sat. Only three players at any position – who saw at least 750 minutes in 2010-11– surpassed him.

This stat is just one way to put words into numbers and tell a story. While a scouting report saying a player pushes the ball is strong evidence, it becomes even more powerful when hard statistical information backs it up.

Three starting one guards followed Jeter in this category: Jose Calderon, Russell Westbrook and Stephen Curry. Jeff Teague, another backup point known for his quickness, placed fifth.

This metric may not always demonstrate speed. Calderon, for example, could excel at getting the Raptors a good shot early in their offensive sets. So while he’s not the type of guard who usually pushes the ball, Toronto still played faster with Calderon in the game.

This stat does get influenced by who shares the court with each point guard. Obviously, you can’t run if your teammates can’t keep up. It also matters who plays the same position for their team.

Nonetheless, NBA agents now have another tool to show how their free agents can impact clubs looking to up the tempo.

The Other Side of Twitter

2011-08-12T08:40:00.000-07:00

I’ve built a sports news gathering organization comparable to ESPN. There are 640 correspondents everywhere from Rio de Janeiro to Rochester, New York to Vilnius, Lithuania. Twenty-four hours per day, seven days a week, the news keep flowing directly to my phone. And it costs absolutely nothing.

Some view Twitter as a way of reaching out to others, which is absolutely true. But there’s another huge component that rarely gets mentioned: Twitter enables you to customize the news that comes to you. Whether I want to learn about a hot high school basketball prospect in New York City, track a minor leaguer in Williamsport, Pennsylvania or learn about emerging sports research or technology, I have a source.

Twitter has major advantages over more traditional ways of gathering information, even Google:

1) Getting the jump on real-time sports information. If there is a big trade brewing or other breaking news, you’ll see it on Twitter well before it hits the major sports websites. Why? Writers like Buster Olney or Ken Rosenthal will usually tweet before they post a story. It takes far less time to blast out 140 characters than an entire article that needs to pass through editors before reaching a webpage.

2) Everything comes to you. The mindset has always been to seek out topics which interest and have value to us. Since Twitter enables you to select followers and subjects that provide news you care about, there is no effort or energy required to find it. Whenever you want it, specialized information is there waiting for you.

3) Going beyond Google. For all its strengths, Google requires multiple steps to finding great sports info. You need to first find the right search terms. When you do, there’s no guarantee Google will have what you need. Even if it does, you may waste time sifting through meaningless links. With Twitter, the posts and links come to you. Your trusted followers do the legwork!

While negative tweets have come back to haunt athletes and other people in sports, it’s not very common. Besides, interacting isn’t necessary. It’s possible to build your news gathering organization without ever posting.

I’ve heard skeptics say “I barely have time to check email, why do I want to get on Twitter?” Unlike email, Twitter isn’t something you need to respond to. I’ll avoid reading Twitter for several days during baseball arbitration season. If somebody wants to contact you via Twitter, they’ll use one of its methods that directs correspondence to your inbox, just like an email.

For sports agents, the other side of Twitter can have tremendous value. And it will only get bigger and better.

The Big Three for Pitchers

2011-08-09T07:16:00.000-07:00

Limit walks, avoid home runs and strike batters out. If pitchers excel in those three areas over time, they will succeed. It is easier said than done.

All three areas are controlled primarily by the pitcher. He does not depend on his fielders for success in them, although home runs can get impacted by his home ballpark.

Sabermetric theory holds that pitchers have limited control over the batting average on balls put into play against them. On these plays, pitchers with a strong defense behind them have a huge edge over those that don’t. Pitchers who perform well in the big three can usually offset poor fielding. And when helped by a strong defense, they can dominate.

Our research shows that very few pitchers shine in all three of these vital areas. For both relievers and starters, we chose levels about 10 percent better than league averages. For relief pitchers, that was 8.5 strikeouts per nine innings and fewer than 3.0 walks and .75 home runs allowed per nine innings in at least 250 career innings pitched. Four active pitchers made the cut: Joakim Soria, Jonathan Papelbon, Mike Adams and Takashi Saito.

The criteria for starters proved even tougher: 500 innings pitched, 7.25 strikeouts per nine innings, and fewer than 2.5 walks and .8 home runs allowed per nine innings. Roy Oswalt stood alone at these levels. Two young starters – Daniel Hudson and Madison Bumgarner – join Oswalt if we drop the innings requirement to 250.

Incredibly, the groups expand by just one player apiece with non-active pitchers included. Reliever Tom Henke and starter Pedro Martinez join them.

Several elite pitchers miss these lists by falling just short in one category, such as Mariano Rivera, C.C. Sabathia and Felix Hernandez.

Prior to free agency and arbitration, we will update this research and vary the criteria to identify other pitchers who stand out in the big three. Why is this important for baseball agents? Because success in these areas makes pitchers more likely to sustain excellence when changing teams.

Beyond the Basics: Sports Analytics and Baseball Free Agency

2011-08-01T10:03:00.000-07:00

Baseball free agency is an exciting time for players, agents and The Sports Resource. Analytics can highlight a player’s achievements, demonstrate and quantify his value, and show his contribution to the club. Unlike arbitration – a process restricted by specific criteria – free agency packages can also focus on what’s ahead with statistical projections.

Since free agency usually involves changing teams, this may add another level of complexity. But the insight gained from this research pays off both immediately and down the road, when it comes time for the next contract. Context impacts statistics far more than many realize, and joining a new club can dramatically change it.

These four key questions address areas where sports analytics can have a major impact beyond the basic components of a free agency package.

1. How will the level of competition affect your player? We elaborated on the AL East's impact on player statistics in the January 2010 issue of The Sports Resource Newsletter. Fortunately, methods exist to predict the impact of competition changes on individual players.

2. In which ballparks would he excel? Actual performance in different ballparks can be valuable. However, players may lack enough plate appearances to make those statistics meaningful.

Park factors give us an indication of how a player will perform when changing stadiums. Everybody knew that Adrian Gonzalez would benefit from leaving PETCO Park for Fenway Park. But it isn’t always that easy. Park factors vary from year to year largely due to weather patterns. Complicating matters further, many stadiums help certain types of hitters more than others. For example, Minute Maid Park is great for right-handed pull hitters with power, but not nearly as great for lefty home run hitters.

3. What impact will a new lineup have? Hitting in a strong batting order has a positive effect on context-dependent statistics like RBI and runs scored, as well as batting average to a lesser extent.

4. Are the potential new teams over or undervalued? Every player wants to win, so this last question is vital. Team records can prove misleading. So it’s better to examine Pythagorean won-lost records, which project winning percentage based on runs scored and allowed. For example, the Astros went 76-86 in 2010 and finished strong. That made them look like a team poised to turn the corner. However, their Pythagorean record was just 68-94. This makes their poor 2011 performance less surprising.

A Closer Look at Goal Scorers

2011-07-10T18:07:00.000-07:00

Sports analytics hasn’t made nearly the same impact on the NHL that it has in the NBA or MLB, but that is changing.

The MIT Sports Analytics Conference held this past March, included a Hockey Analytics Panel. Don Fishman, the assistant general manager and director of legal affairs for the Capitals, had one of the more interesting comments.

“I think even strength stats and power play stats need to be drilled down more in terms of the value of a forward,” said Fishman. “You might have a fantastic forward that the coach is playing zero minutes on the power play. And you might think he’s having a down year. But when you filter out for even strength or shorthanded, he’s actually having a wonderful year. So statistics can lie, if you’re relying on power play stats.”

The overall goal scoring leaders get impacted by both the situations in which they play and how much they play. But a couple adjustments can identify the league’s best scorers.

We ran the numbers for all skaters who saw at least 750 minutes of even strength ice time in the 2010-11 regular season (430 players total). Here is the top 10 in even strength goals per 60 minutes.

Only four of these players also finished in the top 10 for total goals scored: Corey Perry, Jeff Carter, Michael Grabner and Danny Briere. Grabner was most impressive, making the overall list despite only spending 70 minutes all season on the power play. He topped the table above with 1.62 even strength goals per 60 minutes.

Alexandre Burrows also saw limited action on power plays - which hurt his overall statistics – but shined by scoring 1.45 goals per 60 minutes in even strength situations.

Will This Be Your Breakout Year?

2011-06-30T07:45:00.000-07:00

There are many articles out there about finding a job in sports. Almost every sports conference has a panel devoted to this topic. Most of the advice is sound, since it often comes from insiders holding influential positions.

The focus is always on “breaking into sports,” which has become very difficult due to the huge number of people attempting it. There is an alternate approach. I describe it as “breaking out”, like a hitter having a breakout season.

The world has changed. Back in the 1990’s, you had to build and polish a resume with all the right background, send it off to the decision makers, and then hope for the best. Now, technology enables sports outsiders to become insiders without actually breaking in. The barrier is still there, but the right skills and knowledge can streamline the process.

The key step is to put your work out there for all to see. Whether through a website, blog, app or videos, you can gain exposure in sports far easier than ever before. When I wrote The Baseball Perspective in the early 1990’s, it required a graphic designer, print shop and the U.S. Postal Service for a 12-page newsletter to reach 1,000 or so subscribers and prospects. The same process would take far less time and cost almost nothing today!

While your passion and skills should determine what you put out there, I recommend avoiding the edgy sports opinion blogs that are everywhere. Specialize as much as possible. Great examples exist all over the internet. HitTracker is an amazing website that tracks the flight and distance of every Major League home run. Darren Heitner’s Sports Agent Blog is a tremendous resource for insiders, as is Cots Contracts. How about starting a website analyzing NFL coaching decisions? Or how weather conditions impact game outcomes and/or statistics? Everybody wins – you gain exposure and the industry gets another resource.

Internships are great for making contacts and gaining experience. However, your work may not reach the masses or have your name attached to it. With our approach, there’s no limitation on who sees it, especially if you have no problem presenting at conferences and seminars.

Although easier than breaking in, breaking out in sports presents its own challenges. I’m sure the people who run the websites mentioned above had to work long and hard. But fortunately, talented individuals with something valuable to offer now have far more control over their own future in sports. Isn’t that the way it should be?

One Simple Test for Predicting NBA Success

2011-06-23T11:35:00.000-07:00

If a college player can score at a high rate before turning 20 years old, they have a great chance for NBA success.

This quick test was explained a few years ago in The Sports Resource Newsletter. That year, the 2008 NBA Draft had seven players selected who had averaged 20 points per 40 minutes in their final college season before turning 20 years old. Among that group, Derrick Rose, Kevin Love, Michael Beasley and Eric Gordon have emerged as strong NBA players. J.J. Hickson appears headed for a solid career. Jerryd Bayless and Kosta Koufos still have a ways to go.

The next two drafts produced just two “20 under 20” players apiece. All seem destined for excellent careers. The 2009 draft included Tyreke Evans and James Harden, while 2010 had DeMarcus Cousins and Al-Farouq Aminu.

This year’s draft features just three members of the 20 under 20 club. Kyrie Irving and Alec Burks have gotten their share of attention, but a third player isn’t projected to go until the 20’s in most mock drafts. Tennessee’s Tobias Harris actually did the other prospects one better – he averaged 20 points per 40 minutes before turning 19 years old! This hasn’t been done by a drafted player in his final college season since Kevin Durant in 2007.

Burks turned in his own impressive feat by going 20 under 20 in both of his college seasons. He joined Derrick Williams, Jordan Hamilton, Kenneth Faried and Chris Wright as players who did this in seasons other than their final college campaign.

Five Reasons to Activate Your Sponsorships with Statistics

2011-06-21T12:23:00.000-07:00

When fans enter professional sports venues today, they become immersed in technology. While the huge HD video boards grab their attention, they also want unique insight about their favorite team. Diehard fans seek the type of information that only comes from innovative statistical content. So where is it?

In the past year, I have visited numerous NBA, MLB and NFL facilities – including some of the newest and most technologically advanced in the nation – yet not once did they present anything beyond the basic stats.

This is great news for brands looking for creative ways to activate their sponsorships. Fans seek out revealing statistical content. And while teams want to provide it, they may lack the resources or expertise to make that happen.

Here are five reasons why it pays to make creative statistical content part of your sponsorship activation strategy:

1. This approach brings sponsorships to life. Rich statistical content educates fans about the strengths of their favorite team and its players, and sends a crystal clear message. The right metrics won’t confuse fans at all, but build on their connection to both the sponsor and property.

2. Innovative statistical content is ideal for social media. Besides gaining exposure on the video boards, sponsors can also deliver a powerful message in 140 characters, whether by text, Facebook, Twitter or all three mediums. Since fans following a team via social media tend to be its most loyal enthusiasts, brands connect directly to them. Of course, the content must have value.

3. Creative sports statistics are sticky: they get repeated over and over.

4. It is cost effective. Putting such a plan in place will fit well within your activation budget. Brands get ROI for a fraction of what other methods deliver.

5. Analytics tell a great story. Much of the sports industry has yet to discover this. So if you’re looking for fresh ideas, why not make them part of your brand’s story?

Activating sponsorships in this way requires the right content and approach to make it happen. And The Sports Resource has that covered.

Steve Fall's business The Sports Resource has provided NBA, MLB and NFL agents with sports analytics consulting since 1997. Agents use his statistical packages to build player value for contract negotiations, free agency, arbitration and the draft. Last year alone, he worked on over $335 million in contracts. His analytical tools also help companies activate their sports sponsorships.

Properly Valuing Hit Types

2011-06-14T14:23:00.000-07:00

It seems logical that a double is twice as good as a single, a triple three times as good as a single, etc. However, the run values for offensive events vary tremendously from those figures. And they also change over time, depending on the level of offense in the Major Leagues.

Making use of a statistical technique called regression analysis, The Sports Resource calculated run values for five offensive events over two different time frames. Run production dipped from the first timeframe (2000-07) to the second (2008-10), which impacted the results.

The value for triples stands out more than anything else, especially in the more recent timeframe. There’s a large gap between the value of doubles (.75 runs) and triples (1.28), and a much narrower one separating triples (1.28) and home runs (1.42). Common sense would assume that a hit covering 4 bases would carry 33 percent more value than one for three bases. But the actual difference is just 10.9 percent.

What does this mean for agents? Players who hit lots of triples and relatively few homers – such as Jose Reyes and Dexter Fowler – produce more runs than many would think. For example, Reyes' three homers and 11 triples (through June 13) are equivalent to 13 homers and 0 triples.

The other interesting change is the drop in run value for singles. The best possible explanation is that with less overall offense, it’s harder to bring home runners from first base (especially due to the dip in home run rate). In addition, there tends to be fewer runners on base when singles get hit than from 2000 through 2007, further decreasing their value.

Better Sports Statistics and Missed Opportunities

2011-06-06T08:05:00.000-07:00

Moving beyond core statistics has immense benefits for anybody associated with or interested in sports. Advanced metrics – or even relatively simple per minute stats – bring greater insight and understanding.

It takes a look inside the numbers to see the value of players like Joel Anthony. The ABC announcers missed a great chance to do so in Game Two of the NBA Finals. When Anthony made an amazing block, commentator Jeff Van Gundy joked that play-by-play man Mike Breen would have been far more expressive had LeBron James made the play. Did anybody on the broadcast realize that Anthony is the second-greatest shot blocker in Miami Heat history? Don’t the viewers deserve such insight?

Among Heat players with 1000 career minutes played, only Alonzo Mourning (3.67) blocked more shots per 40 minutes than Anthony (3.01). They rank one-two in block percentage as well, which estimates the percentage of opposing two-point shots a player swats while on the court. Unfortunately, that’s not the type of information provided during telecasts, at least not yet.

Anthony is so good defensively that it enables him to contribute despite obvious shortcomings in his game. According to ESPN.com’s Tom Haberstroh, the Heat outscored their opponents by over 19 points per 100 possessions during the regular season when Anthony played with James, Dwyane Wade and Chris Bosh. With this sensational shot blocker positioned down low, the Heat can play tight defense on the perimeter and force turnovers.

Anthony has increased his blocks per 40 minutes figure during the playoffs (2.85 through June 6) compared to the regular season (2.54). He also had Miami’s best postseason plus/minus figure (+88).

As detailed in a recent post, analytics tell a great story. None of these statistics are confusing or difficult to explain, and they show the impact Anthony has on the game.

Agents and clubs officials see the value of advanced metrics, and use them because they increase bargaining power and influence lucrative contracts. It will take some time before sports analytics has a major presence on game broadcasts, stadium and arena video boards, and sports talk radio. But it will arrive, and it won’t be long.

In Defense of Win Totals

2011-06-01T14:00:00.000-07:00

In a recent issue of ESPN the Magazine, Steve Wulf wrote about the debate over pitchers’ win totals. He summarized that wins have far more value when used to evaluate careers than individual seasons.

The Sports Resource put this to the test by comparing pitchers wins – which many statistical experts despise – to Wins Above Replacement (WAR), perhaps the best individual metric for quantifying a starting pitcher’s contributions.

During the 2010 season, the top 10 pitchers in wins had a 3.14 ERA. The best 10 pitchers in WAR posted an outstanding 2.60 ERA. Obviously, the latter group was much stronger. Phil Hughes made the wins group with a 4.19 ERA. The highest ERA in the WAR group was Jered Weaver’s 3.01.

As the timeframe expands, something interesting happens: the gap begins to narrow considerably. After the 0.54 ERA difference in 2010, it drops to just 0.19 over five seasons (2006-10). In a 10-year stretch (2001-10), the gap falls to 0.11 (see chart). While wins never match WAR as an evaluation tool, they become much more valuable.

While run support, defense and bullpen support impact win totals tremendously in one season, those factors tend to even out over time. Rarely will a pitcher receive horrible run support over a 10-year timeframe. His support/luck will eventually improve. Or, if he pitches for a poor team with consistent offensive problems, he could sign as a free agent or get traded to a higher scoring club.

The takeaway message is that agents shouldn’t dismiss win totals completely. Career and multi-year win totals can demonstrate value for starting pitchers, especially in the later arbitration and free agency seasons.

Let the Numbers Tell the Story

2011-05-20T11:31:00.000-07:00

Speaking at the MIT Sports Analytics Conference, Microsoft’s Bruno Aziza explained how “Analytics tell a great story.”

Unfortunately, analytics rarely get used by the mainstream sports media in this way. Instead, metrics often get cherry-picked to fit their story. As a result, the public never gets the entire unbiased view that analytics provide.

For example, the media focused attention on the Miami Heat’s dismal shooting percentage in crunch time, concluding that they couldn’t excel in the clutch. They did shoot one for their first 18 when tied or trailing by three points or less in final 30 seconds. However, most teams have a low success rate in these situations (because defense is tight, referees hesitate to call fouls, etc.). And there are many more clutch situations during games. A balanced analysis would have taken these shots into account as well.

Most importantly, all statistics need a sufficiently large sample before we can reach any conclusions. The Heat had taken just 18 shots which met the specific criteria used. That’s far too few to say that they were poor clutch shooters.

The same thing applies for the hitter who has gone 0-for-10 lifetime versus a certain pitcher. Should the manager decide that he can’t handle the pitcher? Of course not! It would take a sample of at least 50 plate appearances before knowing that for certain.

Presenting a player’s complete statistical profile requires looking at numerous metrics over a significant sample of games. That’s why it’s vital for agents to examine every piece of relevant statistical data possible.

In baseball, all encompassing categories like WAR make valuing players easier. But that is just the start. As explained last year in The Sports Resource Newsletter, role players can have greater impact in select circumstances. Sustained success in high leverage situations also adds to player value.

Yes, analytics tell a great story. But it requires thorough analysis to present the data in a way that builds maximum value for your players.

The Problem with Per Game Statistics

2011-03-31T11:15:00.000-07:00

With so many better metrics available, it’s hard to believe the mainstream sports media still uses per game statistics to evaluate player performance.

ESPN Radio’s Colin Cowherd recently compared Derrick Rose to Allen Iverson. The comparison makes sense on some levels. Both players are shoot first, pass second point guards. Both are incredibly quick and great finishers. Cowherd’s mistake was using per game statistics, which made the players appear closer in performance than they really are.

Cowherd started by saying Iverson had the edge in points per game over Rose in their third NBA seasons: 26.8 to 25.0. This brings up the biggest reason per game numbers fall short: starters vary tremendously in how many minutes they see per game. Iverson played 41.5 minutes per game versus 37.4 for Rose. Using points per 40 minutes to even the playing field, Rose (26.7) has actually scored more than Iverson (25.8).

Rose had a huge edge in assists per game (7.9) over Iverson (4.6) in their third seasons. That difference increases with the more revealing assists per 40 minutes figures: 8.4 to 4.5. Iverson did spend extensive time at shooting guard that year while Eric Snow played point for the Sixers, which impacted his assist numbers. Still, Iverson never came close to matching Rose’s assists per 40 minutes figure in any career season. Rose had also shot for the higher percentage from both two-point (47.2 to 44.0) and three-point range (33.2 to 29.1) in season number three.

Rose had the advantage in John Hollinger’s PER as well, 23.4 to 22.2 over Iverson. Both players have high usage rates – which estimates the number of their team’s plays they use while on the court – of nearly 33 percent. So while they both use a high percentage of their team’s possessions, Rose produces more in those opportunities.

Iverson did have a big edge in steals per 40 minutes in his third season. And while he reached the foul line more often than Rose, they made nearly the same number of free throws per minute due to Rose’s far superior free throw percentage.

Most importantly, Rose is younger than Iverson was in his third season by one year and four months. It makes more sense to compare Rose’s third season to Iverson’s second campaign, which would cause the gap between the players to widen even further. Finally, Rose stands three inches taller than Iverson and weighs 25 pounds more.

While they have some similarities, Rose holds a decisive edge over Iverson at the same stage of their careers. That becomes clear when taking a look beyond their per game statistics.

Iverson was a great player. But in both performance and from a branding perspective, Rose is on track to soar much higher than Iverson ever did.

Sports Statistics as a Marketing Tool

2011-03-16T14:18:00.000-07:00

Politicians discovered the power of numbers long ago. One might say “my administration created one million more jobs than any other in history.” Of course, it only takes a few minutes to pick that apart: How many jobs were lost? What was the net increase? What was the percentage increase? What was the average salary of these created jobs? But by the time his statement gets scrutinized, the politician moves on to the next talking point.

The same thing works in sports. In 2009, when Colt McCoy was a top Heisman Trophy candidate, the media repeated this statement over and over: “McCoy has won more games than any quarterback in college football history.” Wins are powerful, the true currency of sports. The stat spread everywhere and stuck in people’s minds, even though it was not a particular good statistic.

McCoy won more football games partly because he played in so many. Longer seasons and conference title games gave him more opportunity to record victories. Yes, he won the most games of any quarterback, but he was one of 22 starters. And many of those former teammates have joined him in the NFL.

Obviously it took a great quarterback to win that many football games. McCoy had to earn the starting job and keep it four years; no easy feat at a top program. He had a major role in 45 wins. Nonetheless, teams win games, not quarterbacks.

The McCoy stat still got extensive airtime on sports talk radio, highlight shows and game broadcasts. As with a smooth-talking politician, there was little opportunity in those settings to contradict it with objective evidence.

This demonstrates the power of numbers. Since few people effectively use sports statistics as a marketing tool, they present a blank canvas to work with. And the timing couldn’t be better with the rise of social media, when you may only get 140 characters to send a clear powerful message.

If even bad stats have value, can you imagine the impact from innovative statistical content? Finding this isn’t easy – as the best information lies beyond the core stats that dominate the mainstream sports media – but it is well worth the effort.

Summary of the MIT Sports Analytics Conference

2011-03-14T13:07:00.000-07:00

Here’s a wrap-up of the MIT Sports Analytics Conference held earlier this month, with the focus on items of interest to sports agents.

This year’s conference drew 1,500 people to the Boston Convention and Exhibition Center. Representatives from 53 different professional teams attended, according to the organizers. Now in its fifth year, the event has evolved into as much a business conference as an analytics one, with topics about sponsorships and enhancing the game day experience.

Rockets GM Daryl Morey, one of the event’s organizers, pointed out that basketball is a sport that punishes mistakes during the opening panel on developing the modern athlete. He explained how people focus on all the dunks, but mistakes are costly and need to be minimized for success. While players that shoot high percentages and avoid turnovers get little media attention, teams clearly build such contributions into their statistical models and projections.

In the same panel, Morey said that during the NBA Draft process they’re often looking for flaws more than attributes. They identify what problems a player has that they think they can improve upon. This shows why it may be a good idea to address a player’s shortcomings in draft packages and then demonstrate how they will overcome them.

The Baseball Analytics panel also had some interesting exchanges. Tom Tippett, director of baseball information services for the Red Sox, talked about the Carl Crawford contract. Although he lacked the power of most well-paid outfielders, Tippett said that between triples and home runs, Crawford clears the bases about 30 times per year. Tippett said the team also researched how Fenway Park’s dimensions would impact Crawford’s defensive performance.

Both Major League Baseball and the NBA are moving toward having a complete digital record of each game. This creates tremendous opportunities for sports agents and their staffs to analyze and present this data on behalf of their clients.

Relievers and Consistency

2011-01-26T14:30:00.000-08:00

Are relief pitchers more volatile than starters? While that seems to be the case, relievers also get evaluated in much smaller sample sizes.

Statistics usually get expressed in terms of seasons. That works great for starters, but not so well for relievers. Starters throw approximately three times as many innings as bullpen pitchers in a typical season, which gives them far more time to work through their struggles.

For this reason, we compared starters in one-third of a season timeframes to relievers in full seasons. This evens the playing field to help determine which group maintains more consistency.
We identified all relief pitchers that pitched 150 innings or more from 2008 through 2010 and posted an earned run average between 3.00 and 3.50. The starters had to pitch 150 or more innings in 2010 alone with an ERA in that same range. Nineteen relievers and 17 starters met the criteria.

The starters group included CC Sabathia, Cliff Lee, Chris Carpenter and Tim Lincecum. The relievers had pitchers like Francisco Cordero, Huston Street, Brian Fuentes and Jonathan Broxton. The two groups accumulated a comparable number of innings: the bullpen group totaled 3510.2 innings versus 3602.1 for the starters. Both groups had an identical ERA of 3.25.

Based on inspection, the starters appeared slightly more volatile. None of the relievers posted an ERA over 5.00 in any of their annual timeframes. However, two starters did so in their two-month periods: Max Scherzer (6.42) in April/May and Jon Garland (5.16) in June/July. The starters failed to post a single ERA below 2.00, while the bullpen group had three: Jeremy Affeldt (1.73) and Ryan Franklin (1.92) in 2009 and Chris Perez (1.71) in 2010.

The best way to measure consistency is through standard deviation, which simply measures how much figures differ from the average. The standard deviation for the starters (.635) was slightly lower than the relievers (.677). Basically, there was very little difference in consistency between these groups.

The starters also had one huge advantage. While the relievers had an entire offseason in between their seasons, the starters flowed directly from one two-month period into the next. In several cases, the relievers changed teams and/or leagues during the offseason, making it even harder to sustain consistent performance.

Before making any conclusions, more data should be examined beyond this rather small study. It would also be valuable to examine statistics like OPS allowed, since ERA does not account for how relievers pitch with inherited runners.

Nonetheless, it appears that what some perceive as lack of consistency has more to do with the limited number of innings relievers pitch per season.

The Truth about Strikeouts

2010-09-20T12:49:00.000-07:00

Managers hate when hitters strike out. The mainstream media often criticizes high-strikeout players like Mark Reynolds, much more than it should.

When it comes to winning and losing, strikeouts by hitters aren’t much more costly than other types of outs. While pitchers’ strikeouts have a major effect on run scoring, the same doesn’t hold true for hitters.

This insight comes from research using an advanced statistical technique called regression analysis. Without getting into the details, regression analysis determines how well statistics correlate with each other. Pitchers’ strikeouts have a much greater correlation with run prevention than hitters’ strikeouts have on run scoring.

How can this be? In general, hitters who strike out a lot also hit home runs and draw walks. On the other hand, strikeout pitchers limit offense better on average than pitchers who miss bats less often. They are also less dependent on their defense to make plays behind them.

Whether in arbitration or free agency, baseball agents can emphasize the value of high strikeout pitchers. And if you represent a high-strikeout batter, exhibits with this information provide hard evidence in his favor.

Opportunity and Statistics

2010-09-16T09:14:00.000-07:00

Most sports statistics – especially the ones that get attention in the mainstream media – are opportunity based. Other metrics filter out opportunity, and they carry tremendous comparative value.

Many still fixate on per game numbers, and they don’t begin to tell the story for players like DeJuan Blair. His 7.8 points per game and 6.4 rebounds per game in 2009-10 look pedestrian. However, Blair posted these numbers in limited opportunities – playing just 18.2 minutes per contest.

Rebounds per 48 minutes is not impacted by how much players see action. Among NBA players with at least 750 minutes played, Blair ranked sixth in rebounds per 48 minutes (16.9). He topped all NBA players in offensive rebounds per 48 minutes (6.43), and remember that he was a 20-year-old rookie!

Even the offensive rebounds per 48 minutes statistic gets impacted by opportunity. Some teams play at a faster pace than others, and some miss more shots. Their players have more opportunities to grab offensive boards. The Spurs played at a slower pace than most teams and had the NBA’s sixth-highest shooting percentage. So these factors hurt Blair, yet he still out-rebounded everybody at the offensive end.

The best metric to show Blair’s rebounding excellence is rebound rate, John Hollinger’s measurement for the percentage of missed shots that a player rebounds when he’s on the court. Blair had a 16.0 offensive rebound rate last season. To put that in perspective, NBA teams grab 26-27 percent of available offensive boards on average. The Golden State Warriors had an offensive rebound rate of 20.9. Blair fell just 4.9 short of that figure, by himself.

While playing his final season at Pittsburgh, Blair put up unbelievable stats in this category. Despite playing in the rugged Big East, his 23.6 offensive rebound rate topped the nation’s next closest player by 5.0. Blair even surpassed the team figure for six Division I colleges.

So how did a player – who can out-rebound an entire team – last until the 37th pick of the 2009 NBA Draft? It’s hard to say. Blair’s 2008-09 rebounds per game figure (12.3) looked good but unspectacular, which may have been a factor. Of course, he played only 27.3 minutes per game on a very slow-paced team. Only adjusting his numbers for opportunity made Blair stand out.

Caution: Falling Offense

2010-09-14T09:41:00.000-07:00

Remember 1992? That was the last time National League offense had gone lower than the current level of 4.36 runs per game. The same goes for the American League, which has seen an even sharper scoring drop-off since last season. AL teams averaged 4.82 runs per game in 2009. That figure had plunged to 4.45 through September 14. The NL had a more gradual decline from 4.43 runs per game last year to 4.36.

This presents a challenge for agents with arbitration-eligible and free agent position players this offseason. Clubs will no doubt pull out comparables from recent seasons when the run context was substantially higher.

Fortunately, there is a solution. Agents can adjust for the decreased offense in the same way economists do so for inflation. The Sports Resource has built a statistical model that adjusts for run context, which helps your position player clients when scoring drops.

You can even turn the scoring trend into a positive for hitters: some of this season’s individual achievements will stand out even more at contract time. For example, should Jose Bautista reach 50 home runs, he will match a feat last accomplished in 1990. Look for another post on this topic in the weeks ahead.

Who is Today’s Jose Cruz?

2010-09-13T08:44:00.000-07:00

I drove past the Astrodome last week, seeing the old stadium for the first time. Now dwarfed by the adjacent Reliant Stadium, it brought back memories of 1-0 victories thrown by great Houston pitchers like Nolan Ryan and J.R. Richard.

Through most of its history, the Astrodome was an awful place to hit a baseball. Jose Cruz had the misfortune to play there in the 1970s and 80s. Had Cruz played in Fenway Park or Wrigley Field back then, he may be remembered as one of his era’s greatest hitters.

Cruz hit 59 career home runs in his home parks and 106 in road games. Although he started with Cardinals and ended up with the Yankees, Cruz had 83 percent of his career plate appearances for the Astros.

During his peak from 1976 to 1986 – when he played exclusively for the Astros – Cruz had a 128 OPS+ according to BaseballReference.com. Since this metric adjusts for both the league average and a player’s ballpark, the Astrodome’s negative impact gets stripped away. Cruz ranked 24th in OPS+ among players with 2500 plate appearances from 1976-86, finishing in a group of more heralded players like Dale Murphy (129 OPS+), Cal Ripken Jr. (129), Kirk Gibson (128), and Dave Parker (128).

In that same timeframe, Cruz hit 100 homers and stole 250 bases. Only Andre Dawson, Rickey Henderson, and Davey Lopes joined him at those levels. Cruz reached base 2412 times, more than all but five other Major Leaguers from 1976-86.

While there are no stadiums like the Astrodome today, Safeco Field and PETCO Park have a comparable impact on offense. Although we now have tools that few knew about during Cruz’s playing days to adjust for run context, they still get limited attention.

Ballparks have a huge impact on statistics, yet many fail to take this into account in solving the value puzzle. Examining park effects is vital for not only showing a player’s true performance level, but where his career is headed.

Hot Topics from the SABR Convention

2010-08-17T13:45:00.000-07:00

The Society for American Baseball Research Convention, held earlier this month in Atlanta, had something for every diehard fan. I’ll focus this post on topics of greatest interest to baseball agents.

Vince Gennaro, a consultant for Major League teams and author of the book “Diamond Dollars”, gave a fascinating talk on the economics behind midseason trades. He pointed out that a player’s true value is different for every team. For example, Cliff Lee had a much greater value to the Rangers than the Mariners this season.

Gennaro described how much team revenues get impacted by winning. Just reaching the postseason has a $25-to-$50 million benefit to teams. And it has a multi-year effect for up to five seasons!

It occurred to me that if the playoff races stay close, a number of arbitration-eligible players could make the difference between their club earning a playoff spot and missing out. That would carry some weight this offseason!

The New Technologies and Baseball panel was a serious eye opener. Based on the capabilities of the data becoming available, I wouldn’t be surprised if terms like “launch angle” become common in the next five years. It’s now possible to analyze the flight of both a pitched and batted baseball. Want to know which batters hit the ball the hardest? It’s all there. Among numerous other applications, this information could be used to determine whether a hitter is truly in a slump or hitting the ball just as well but experiencing bad luck.

Physicist Alan Nathan used PITCHf/x data to show the brilliance of Mariano Rivera. But he debunked the theory that his pitches have “late break”. This is actually an illusion caused by the fact that one of Rivera's cutters breaks about five inches more than his other cutter.

J.C. Bradbury, author of “The Baseball Economist”, gave a great presentation on pitch counts and days of rest. He showed data revealing that – contrary to popular opinion – pitch counts have remained stable since 1988. But minimum pitch counts by starters have actually increased, possibly as a result of managers looking to ease the workload on their bullpens.

Bradbury also found that there was little difference in performance by pitchers working on just three days rest versus four. This always becomes a hot topic in the postseason.

In general, the conference demonstrated how much research is out there to help agents build value for their players.

Keeping it Simple

2010-07-27T11:28:00.000-07:00

I have been reading “The House Advantage” by Jeff Ma. The book explains the importance of incorporating data into the decision process. Ma uses examples from his background in sports and as a member of the MIT blackjack team; he was the central character in the best-selling book “Bringing Down the House”.

In one chapter, Ma writes about the approach he takes when consulting with businesses, whether sports-related or not. He summarizes it without using any complex statistical terms, but as taking information from the past to make decisions about the future. Whether in sports or business, that process creates an edge.

This applies to most sports decisions, whether for free agency or the draft. What trends are in a player’s past that bode well for their future? That track record may be long and consistent with plenty of data, as with Albert Pujols or Tim Duncan. Or it could be brief, such as the case of a young lefty relief specialist or a college freshman entering the NBA Draft. Generally speaking, the more the data, the easier it is to project performance going forward.

The quality of the data is also important. When projecting Ubaldo Jimenez’s performance for the season’s final two months, his impressive won-lost record through July is not all that helpful. His ERA is more valuable, but still not the best predictor. The best projections come from examining Jimenez’s walks and home runs allowed, the percentage of batters he has struck out, and factors like left on base percentage and batting average on balls in play. Performance in previous seasons matters too, as that is all part of his track record.

Digging Deeper

2010-06-15T12:02:00.000-07:00

This time of year, you need to look beyond the core statistics to determine true performance. Last June, I tagged Jorge de la Rosa as a pitcher primed for a turnaround. At the time, he was 2-7 with a 5.81 ERA. He finished 16-9 with a 4.38 ERA. I also predicted two arbitration eligible starters would head in opposite directions.

This year, Randy Wells is a candidate for a huge turnaround. His mainstream stats don’t look good (3-5, 5.15 ERA). But a pitcher’s actual performance is better evaluated with advanced metrics, especially in timeframes of less than half a season.

Wells has struck out batters more often than he did last season, when he went 12-10 with a 3.05 ERA. He has also given up home runs and walks less often than in 2009. The problem has been his high batting average on balls in play. His .359 BABIP is 65 points higher than last season. While he has allowed more line drives, this also shows that he has experienced some bad luck and/or poor defense behind him. A low left on base percentage demonstrates that Wells’ hits allowed have been poorly timed. Neither trend should hold up for the entire season, so expect his ERA to improve.

The disconnect between core statistics and advanced metrics isn’t always this great, but it always exists to some extent. Knowing this can not only build value, but it helps predict future performance as well.

Stepping Up

2010-06-07T09:07:00.000-07:00

Caron Butler's team only lasted one round in the NBA Playoffs, but he did his part at the offensive end. Butler increased his points per 48 minutes figure by 7.2 - from 20.8 in the regular season to 28.0 in the postseason - to top all NBA players (through June 7, minimum: 150 playoff minutes).

Paul Millsap came next at 6.6 more points per 48 minutes, followed by Luc Richard Mbah a Moute (+5.6), Jameer Nelson (+5.5), Russell Westbrook (+5.3), Deron Williams (+5.0), Jason Richardson (+4.5), Goran Dragic (+3.6), Derrick Rose (+3.3), and Dwyane Wade (+2.8).

Among players who have drawn the media spotlight this postseason, Kobe Bryant (+2.1) had the 12th-greatest increase and Rajon Rondo (1.4) placed 16th. Ray Allen has actually scored slightly less per 48 minutes (-0.3). LeBron James saw a drop of 3.4 points, but had still posted the playoffs' fourth-highest points per 48 minutes figure (33.4).

Surprisingly, neither NBA Finals participant had a player in the top 10. The Lakers had two more players - Derek Fisher and Pau Gasol - join Bryant in the top 20. Rondo was the sole member of the Celtics in the top 20.

One key point to make is that most players see their scoring rate dip in the playoffs, mainly because the pace slows. Among the 94 players to see at least 150 playoff minutes, only 32 increased their points per 48 figures.

After the NBA Finals, we will examine points per possession, which adjusts for differences in game pace. We will also analyze advanced metrics to demonstrate who stepped up their all-around game in the playoffs.

Cox and Torre Have Lived Parallel Lives

2010-06-02T13:01:00.000-07:00

Along the lines of the Abraham Lincoln and John F. Kennedy list of bizarre coincidences, Joe Torre and Bobby Cox have plenty of their own. This has nothing to do with sports analytics, but had to post it anyway:

Joe Torre has three letters in his first name and five in his second name.
Bobby Cox has five letters in his first name and three in his second name.

Torre played for the Braves and managed the Yankees.
Cox played for the Yankees and manages the Braves.

Torre originally signed with the Braves and manages the Dodgers.
Cox originally signed with the Dodgers and manages the Braves.

Both Cox and Torre played third base in the Major Leagues.

Both played for teams in New York City: Torre with the Mets, Cox with the Yankees.

Both managers had their first full season in 1978.
Torre finished last in the NL East.
Cox finished last in the NL West.

Both managers had their second full season in 1979.
Torre finished last in the NL East.
Cox finished last in the NL West.

Torre managed in New York (with the Mets), left to manage other clubs, and returned to New York (with the Yankees).
Cox managed in Atlanta, left to manage another club, and returned to Atlanta.

Torre replaced Cox as Braves manager before the 1982 season.

Both skippers left managing in the mid-1980s to pursue other opportunities: Torre as a broadcaster and Cox as a GM.

Torre had two managerial stints in New York. He struggled in the first one and became one of the all-time greatest managers in the second.
Cox had two managerial stints in Atlanta. He struggled in the first one and became one of the all-time greatest managers in the second.

Both managers have made 15 postseason appearances, more than any other manager in Major League history.

Both managers have had streaks of 14 consecutive playoff appearances, more than any other manager in Major League history.

Adjusted Double-Doubles

2010-05-20T15:50:00.000-07:00

No matter how well a player performs, playing time has a huge impact on per game statistics. Per game metrics remain the favorite of the mainstream sports media, even though playing time varies widely among those considered “regulars.”

A total of 15 NBA players averaged a double-double in 2009-10. The list includes several current and former All-Stars: Tim Duncan, Carlos Boozer, Chris Bosh, Dwight Howard, Pau Gasol, Steve Nash, Deron Williams, Chris Paul, Andrew Bogut, David Lee, Zach Randolph, Gerald Wallace, Troy Murphy, Kevin Love, and Joakim Noah.

Love’s achievement was most impressive considering he only played 28.6 minutes per game. The other 14 averaged 35.1 minutes per contest. Love became just the second active player to average a double-double in less than 30 minutes per game. Lee, the only other, matched his feat in 2006-07. Among the 31 total players to do this in NBA history, Love was the youngest ever.

It’s nearly impossible to average a double-double in less than regular action. Several more players could have reached this level had they seen as much playing time as the first group. So here is a group of additional players that projected as double-double guys based on points, rebounds, and assists per 35 minutes. This adjustment puts them on equal ground with the earlier group, which saw that much game action on average.

Lamar Odom
Brendan Haywood
Emeka Okafor
Udonis Haslem
Samuel Dalembert
Drew Gooden
Shaquille O'Neal
DeJuan Blair
Serge Ibaka
Kris Humphries
Nazr Mohammed
DeAndre Jordan
Louis Amundson

Expect the younger players on this list – Serge Ibaka, DeAndre Jordan, and DeJuan Blair – to emerge as they receive more burn in the upcoming seasons. All they need is additional PT to post big per game numbers.

MIT Sports Analytics Conference

2010-03-10T16:56:00.000-08:00

Here’s a brief recap on last weekend’s MIT Sports Analytics Conference. The conference doubled in size from last year, and again delivered invaluable information for sports agents. This post only covers highlights on basketball topics.

One of the most interesting exchanges dealt with how NBA teams value clutch performance. Rockets GM Daryl Morey said he likes when players have shown strong clutch performance in the past, but he wouldn’t spend millions on a player based on that. Mark Cuban countered by saying he would pay for it, and cited Jason Kidd as one example.

Morey later explained that their research revealed how well Kevin Martin had performed against tough opposing defenses before trading for him at the deadline. Agents may want to emphasize this point for their free agents who excel in this area.

Cuban believes certain NBA teams have an advantage in analytics. Why does he think that? For one, he examines the combinations that some teams place on the court. By comparing that to data that the Mavericks research, he knows which clubs are informed and not, as some of these lineups have poor track records.

Morey also said the Rockets thoroughly research how well a player will perform in their system versus with their current team before acquiring them.

Agents can feel free to contact me for a more detailed rundown on the conference.

The Power of Park Effects

2010-01-05T12:30:00.000-08:00

Park effects have a huge impact on baseball statistics. Yet this area is confusing and often misunderstood. Here are some key points on park effects that agents may find useful for free agency and arbitration.

Beware of Reputations: Citizens Bank Park is thought to be a hitter’s paradise. Yet the numbers don’t back that up. According to the Bill James Handbook, the Phillies home ballpark increased home runs by just 1 percent last year. The impact was far greater from 2007 through 2009 when the park upped homer frequency by 14 percent. But even in this time frame, run production only increased by 3 percent. Now here’s the real shocker: After all the talk about the early season home run barrage in the new Yankee Stadium, the park decreased run production by 4 percent in 2009.

Avoid “One Size Fits All Park Factors”. Ballparks affect different players in different ways. Minute Maid Park is a good park for right-handed home run hitters, but not for left-hand hitters with power. Chase Field, which greatly increases doubles and triples, makes a great fit for gap hitters with speed.

Park Factors Change from Year to Year. Weather patterns and other factors influence park effects. Turner Field increased run scoring by 6 percent in 2008. Last season, when Atlanta had a cooler than usual summer, it decreased scoring by 10 percent.

Don’t Buy the Road Stats Argument. In some cases, teams may point out that a player had comparable numbers in both home and away games to show that his home park did not hurt his statistics. But most players have better numbers at home than on the road, probably due to park familiarity and the negative effect of travel on away stats. Ballparks impact statistics whether or not a player’s home and road numbers look similar.

Getting Defensive about Offense

2009-08-17T13:12:00.000-07:00

Believe it or not, one of biggest factors in any offensive comparison is defensive position. Let’s take two of the top contenders for the American League Most Valuable Player: Mark Teixeira and Joe Mauer.

Both have had extraordinary seasons. Teixeira has a .939 on-base plus slugging percentage, while Mauer has a 1.071 OPS. The gap between the players shrinks because Teixeira owns superior bulk, having contributed at that high offensive level in 523 plate appearances over 114 games, versus 405 plate appearances and 92 games for Mauer.

Defensive position makes a huge impact on this comparison. American League first basemen have averaged an .837 OPS this season. Teixeira tops that figure by 102 percentage points. AL catchers own a .726 OPS. Mauer exceeds the average by 345 percentage points. His production relative to position surpasses Teixeira by 243 points.

Any time a hitter posts big offensive numbers at catcher, second base, shortstop or center field, he provides immense value to his team. Why? Offense is less abundant at these defensive spots. So assuming the player fields his position adequately, his team gets superior offense where most teams get far less production.

Since team performance plays a role in MVP selections – and the Twins are a long shot to make the postseason – Mauer may not win the award. But his offensive value far exceeds Teixeira’s at the moment.

What Core Numbers Don’t Reveal

2009-08-11T15:16:00.000-07:00

On the surface, Ricky Nolasco’s statistics (8-7, 4.86 ERA) make his season look far worse than his strong 2008 campaign (15-8, 3.52). Believe it or not, he has actually pitched better this year than last.

Nolasco has struck out 23 percent of the batters he’s faced in 2009, compared to 20.9 a year ago. In fact, he has a chance to become the first Marlins ERA qualifier to strike out over a batter per inning. He’s also allowed home runs less often than during 2008. Although his rate of unintentional walks has risen, Nolasco has fared better in the statistics where he has the most control.

The biggest reason for his higher ERA this season is poor luck. The Marlins righthander has allowed a .337 batting average on balls in play. Without getting into the detailed explanation, this means that the Florida fielders have converted an extremely low percentage of batted balls into outs with Nolasco on the mound. He also has the NL’s lowest left on base percentage (63.3), another example of poor luck. As the season progresses, these trends should become less extreme and Nolasco’s ERA will therefore improve.

A pitcher's statistics get impacted by the quality of hitters they face as well. The batters who have hit against Nolasco owned a higher combined OPS (.746) than those faced by all but one other NL pitcher.

When you take an in-depth look at his numbers, Nolasco has improved from last season. Especially in time frames less than a full season, core numbers can prove misleading.

Under the Radar

2009-07-07T08:54:00.000-07:00

He is one of the game’s top home run hitters. He knocks balls over the fence at a greater rate per plate appearance than Albert Pujols, Manny Ramirez and Alex Rodriguez. In fact, among active players with 1,000 career plate appearances, only Ryan Howard surpasses him in this category. Who is that player? Would you believe Marcus Thames?

Thames has drilled 37.2 career home runs per 600 plate appearances. That trails only Howard (42.3) among active players. Pujols (36.6), Rodriguez (36.6) and Jim Thome (35.8) round out the top five.

Thames gets little media attention because he has never received enough playing time to post a 30-homer season. This year, after missing a month and a half, he had launched 7 long balls in 119 plate appearances (through July 6). That projects to 35.3 home runs per 600 plate appearances, not far off his career figure.

While Thames’ limitations keep him from playing more often, baseball’s statistical conventions hurt him as well. When it comes to hits, baseball uses a percentage stat (batting average). However, home run power always gets expressed as a whole number. There’s no reason we can’t show it as a percentage or rate, besides the fact that years of conditioning have trained us to do otherwise.

Such a change also helps hitters like Luke Scott. His 16 home runs tied for 26th in the Major Leagues. But he ranked ninth with 39.2 homers per 600 plate appearances.

While the media won’t start expressing home runs this way any time soon, such rankings can help agents immensely in arbitration and free agency.

Regression to the Mean: And What it Means for Agents

2009-06-23T10:51:00.000-07:00

There’s a force more powerful than the Steelers defense or a monster slam from Shaquille O’Neal. It explains everything in sports from the sophomore jinx to unlikely postseason heroes to why slumps occur after hot starts.

“Regression to the mean” profoundly impacts sports statistics, yet you’ll never hear it mentioned on a game broadcast. Regression to the mean holds that as the sample size for a statistic increases, the amount the statistic varies within a group will decrease. In other words, the number of outrageously good and bad percentages will decrease as the season progresses and players/teams see more game action. They approach the mean, just another word for average.

Take the amazing 8.9 yards per carry average posted by Cowboys running back Felix Jones in 2008. He went down for the season after just 30 rushing attempts. Had he not gotten hurt, his yards per carry average would have dropped sharply. Not because defenses would focus on stopping him – Dallas had more dangerous offensive players – but due to regression to the mean.

Among NFL running backs that had at least 100 attempts during the 2008 season, none managed even 6 yards per carry. But many backs had 30-carry stretches when they approached Jones’ figure. The Cowboys rookie just happened to post his average over the course of a shortened season, before regression to the mean could rear its ugly head.

In fact, in the regular season’s final two games, the Giants’ Derrick Ward had a 9.7 yards per carry average in exactly 30 carries. For the season, Ward’s 5.6 average topped all backs with at least 100 rushing attempts. While an impressive feat, that’s a big drop-off from 8 or 9 yards. All caused by regression to the mean.

As an agent, it pays to understand this concept. Should one of your clients jump out to hot start, teams will tend to overvalue him. But he’s likely to see his statistics fall off. It works the other way too. When your player struggles early in the year, his numbers should improve, provided he continues to see game action.

Regression to the mean explains team performance as well. In 2007, four NFL teams won at least 13 games: the Patriots (16-0), Cowboys (13-3), Packers (13-3) and Colts (13-3). They combined for a sizzling .859 winning percentage and 55-9 record. This year, they had a combined 38-26 record and .594 winning percentage. And three of the four teams missed the playoffs! Injuries and other negative factors hit these teams hard, but so did regression to the mean.

In baseball, every postseason brings unlikely heroes. Why does this happen? The short postseason creates a small sample of games where average players can put up great stats before regression to the mean brings them down to earth. The same concept explains why some stars struggle in the postseason. That has little to do with choking – as some may claim – and everything to do with small sample sizes.

A Better Metric for Strikeouts

2009-05-28T12:45:00.000-07:00

Even with sports analytics impacting baseball more every season, opportunities still exist to upgrade some mainstream statistics.

High strikeout totals grab media and fan attention, despite research showing that strikeouts are only slightly less damaging to offensive production than other outs. Nonetheless, since they get perceived as a negative, we should at least evaluate them in the proper context.

Ryan Howard (54), David Wright (50), Adam Dunn (50) and Prince Fielder (48) rank third through sixth in NL strikeouts (through May 27). But their strikeout rates – the percentage of plate appearances that end in a strikeout – rank much better. Howard drops from third in total strikeouts to fifth in strikeout rate. Wright falls from fourth to ninth, and Dunn goes from a tie for fourth with Wright to 10th. Fielder falls all the way from sixth to 13th. All these players get lots of plate appearances, which makes them look worse at making contact than is actually the case.

Strikeout rate also makes a great stat for showing which players shine at making contact. Miguel Tejada leads the NL with a 5.6 percent strikeout rate. Carlos Lee (7.9) and Albert Pujols (7.9) place fifth and sixth. By the way, this measure is superior to the more commonly used at-bats per strikeouts, which penalizes hitters who draw more walks than other players.

Strikeout rate also works as a better tool for evaluating pitchers than the more commonly used strikeouts per nine innings. For example, Zack Greinke (9.7) and Joba Chamberlain (9.1) have comparable strikeouts per nine innings figures. But Greinke (28.6) owns a far superior strikeout rate than Chamberlain (22.8). Why? Chamberlain has faced more batters per inning because he has allowed hits and walks more often than Greinke. This gives him more chances to log strikeouts each inning. But why should Greinke’s pitching effectiveness hurt him in this statistical category? If strikeout rate gets used, this isn’t a problem.

The LeBron Stoppers

2009-05-22T08:14:00.000-07:00

In the coming years, Eastern Conference foes will be searching for ways to slow down the Cavs and LeBron James. Defenders effective against them will have tremendous value. Which five-man player combinations excelled at doing this in 2008-09?

The units in the chart had the best defensive efficiency against the Cavaliers with James on the court this season. Defensive efficiency is the number of points allowed divided by defensive possessions times 100.

This list includes the five-man combinations that posted a defensive efficiency below 95 while on the court versus Cleveland and James. To put that figure in perspective, the league had an average defensive efficiency near 108. The average against the Cavs with James playing was 115.1. Among the 71 units that faced them for at least 10 minutes this season, only these 16 posted a defensive efficiency under 95.

The red ink shows the player likely to have guarded James from each unit. In some cases, teams may have zoned or rotated defenders on James.

Units (Team) Def. Eff.
1 Brown-Hamilton-McDyess-Prince-Stuckey (Detroit) 79.4
2 Artest-Battier-Brooks-Yao-Scola (Houston) 80.5
3 Ford-Granger-Hibbert-Jack-Murphy (Indiana) 82.2
4 Deng-Hinrich-Nocioni-Rose-Tyrus Thomas (Chicago) 82.4
5 Brand-Iguodala-Miller-Williams-Young (Philadelphia) 82.6
6 Blake-Fernandez-Outlaw-Przybilla-Roy (Portland) 84.0
7 Iverson-AJohnson-Prince-Stuckey-Wallace (Detroit) 84.8
8 Hawes-Jackson-Martin-Nocioni-Thompson (Sacramento) 86.2
9 Howard-Lee-Lewis-Nelson-Turkoglu (Orlando) 87.1
10 Anthony-Chalmers-Haslem-Marion-Wade (Miami) 88.0
11 Brown-Hamilton-Iverson-Prince-Wallace (Detroit) 88.9
12 Azubuike-Biedrins-Ellis-Jackson-Morrow (Golden State) 89.3
13 Alston-Howard-Lee-Lewis-Turkoglu (Orlando) 90.5
14 Bibby-Horford-Johnson-Smith-Williams (Atlanta) 91.4
15 Deng-Gooden-Rose-Sefolosha-Tyrus Thomas (Chicago) 92.6
16 Dalembert-Evans-Green-Iguodala-Miller (Philadelphia) 93.3

Detroit had three of the top 11 defensive units versus James and the Cavs. The only player in common to all three groups was Tayshaun Prince, who guarded James himself. The Magic had the ninth and 13th place units, which could loom large in the Eastern Conference Finals. The only different between their two groups was at point guard with Rafer Alston taking over for the injured Jameer Nelson to join their other four starters. The Sixers and Bulls also had two units apiece on the list.

While these combinations excelled defensively against Cleveland with James on the court, they may not have limited his scoring. But they slowed down the other four players enough to succeed.

Opportunity and the NBA

2009-05-14T10:54:00.000-07:00

When the Hawks managed a stop in their series versus the Hawks, there was Anderson Varejao battling to keep the ball alive any way possible. He didn’t always grab the board, but it deflated Atlanta when he did. All that work on defense, only to have to start with a fresh shot clock.

Watching players like Varejao and Chris Andersen impact their teams makes you wonder how many players like this are out there looking for opportunities: guys that rebound, defend, hustle and manage to score some without getting plays called for them.

One thing our research has revealed is that teams will always find top scorers. That’s not always true with rebounders. According to the numbers, getting an opportunity is much tougher for these players.

This season, 128 of the 329 NBA players that saw 500 minutes of action averaged 20 points per 48 minutes. Meanwhile, 105 of those 329 averaged 10 rebounds per 48 minutes. What’s interesting is the scorers averaged 2188 minutes played, 567 more minutes than the rebounders (1621). While just 15 of these scorers saw less than 20 minutes per game, 42 of the rebounders failed to get that much court time.

This effect gets even more extreme if we make the groups more selective. The league had 45 players that saw 500 or more minutes average 25 points per 48 minutes. A near equal number of players – 49 of them – had 13 boards per 48 minutes. This group of scorers averaged 2378 minutes played compared to 1628 for the rebounders. In other words, the extra rebounds did nothing to generate more playing time. They averaged only 7 more minutes per contest than the previous 10-rebound group. But the new scorers group averaged 190 more minutes.

Here’s the amazing thing: none of the 25 points per 48 minutes scorers failed to see less than 20 minutes per game. Lou Williams had the lowest figure at 23.7 minutes per game. The rebounders group had 15 players that saw less than 20 minutes per game.

So the message is clear: If you score – even as a bench player – some team will get you playing time. Rebounders have a tougher time. While quality scorers certainly impact the game more than quality board men, the gap isn’t large enough to explain this type of discrepancy.

A look back at the 2003-04 season produced comparable results. This time, the list of underutilized rebounders (13 rebounds per 48 minutes in less than 20 minutes per game) included two interesting names. The first, David West, later became an All-Star. Chris Andersen was the other. Not all the other players emerged – some have since left the game – but there are more Andersens and Varejaos out there. Is there a team that will give them a chance?

10 Hot Topics from the MIT Sports Analytics Conference

2009-03-29T19:21:00.000-07:00

For the second consecutive year, I attended the MIT Sports Analytics Conference on March 7. The event drew a large crowd of sports insiders, researchers, media and students. Among all the great information coming from the speakers and panelists, these were my top 10 highlights:

1. Dean Oliver of the Denver Nuggets, a member of the Basketball Analytics panel, pointed out that the NBA teams actively involved in analytics are now residing in the upper part of the standings.

2. Mike Zarren, the Celtics Assistant Executive Director of Basketball Operations, believes that the ability to communicate what numbers mean is as important as the statistics themselves.

3. Rockets GM Daryl Morey explained trading the #8 pick of the 2006 draft (which became Rudy Gay) for Shane Battier, drawing a parallel from the financial world. “You have to take risks. In a game where one of 30 teams wins [a championship], you can’t just try to beat the market index.”

4. Mark Cuban stated that chemistry is important for all businesses. In this respect, other businesses aren’t all that different from basketball.

5. Cuban estimates that one win is worth a half million dollars in revenue. He added that the most profitable type of club is one in rebuilding mode, with low salaries and a low win total.

6. Most fans, media and sports insiders feel strongly that players who hit a shot are more likely to connect the next time they shoot. How often during March Madness did announcers state that a premier shooter could start to go off after nailing a three-pointer? But John Huizinga, a college professor as well as Yao Ming’s agent, presented compelling evidence against such a theory. He showed that players are more likely to miss their next shot after sinking one.

7. The Baseball Analytics panel talked about how new metrics for evaluating fielding have made an impact on the game. John Dewan of Baseball Info Solutions pointed out that the difference between baseball’s best defensive team in 2008 (the Phillies) and the worst (the Royals) amounted to 130 runs. The offensive gap between the top run-scoring club (Texas) and the lowest (San Diego) was 260 runs. David Pinto followed that by explaining how defense turned the fortunes of the Devil Rays pitching staff in one year.

8. Shiraz Rehman, the Diamondbacks’ Director of Baseball Operations, spoke about the approach to valuing players. “The understanding of replacement value is becoming more apparent. What do we have to spend to get x value over replacement level?”

9. Speaking as a panelist on the “Value of Icon Players,” Celtics star Ray Allen fielded a question about individuals and team performance. He believes basketball players have to be a little cocky and crazy. He said that “if you do it the right way, individualism will make the team better.” He added that “having a great teammate is the best thing in sports.”

10. Despite the economic downturn, Tim Romani of ICON Venue Group sees stadium naming rights as a great way to activate a brand. He sighted the 02 Arena in London as a great example of what’s possible.