The Best Defense for Arbitration

One of our recent posts detailed vital differences between baseball arbitration and free agency. Here’s another major one: an opposing party isn’t trying to discredit exhibits in your free agency packet. That is true in an arbitration hearing.

The Sports Resource strives to make agents arbitration briefs “rebuttal proof” by going on the offensive. One way to do this is make key points at least three different ways. This strengthens your arguments and defends them against the close scrutiny that always follows your presentation.

Even a powerful arbitration exhibit can get refuted when the club has its rebuttal time. Dismissing a point supported by multiple exhibits is much more difficult.

If one theme focuses on a position player’s clutch performance, we first demonstrate with core numbers. For example, we could show a strong slash line (batting average/on-base percentage/slugging percentage) late in close ballgames. Another exhibit could detail all the player’s clutch achievements in the platform season: extra inning hits, game-winning RBI, etc.

Advanced metrics like win probability added – a great tool for this purpose – can drive home the point even further. Finally, we may include pertinent quotes from baseball insiders like teammates, coaches, or even front office members. This not only supports the numbers, but makes them come alive.

It’s never easy. The player may not have excelled in all these statistical categories, or he may have shined in some during the platform year but not his career timeframe. We find the right balance, avoiding anything that appears contrived, and build support for the main themes of the case.

Arbitration and Free Agency: A Key Distinction

Baseball arbitration and free agency differ tremendously in one respect: free agency is about what a player will achieve while arbitration focuses on what he has done.

Future projections – based on past performance – play a role in free agency. But projections aren’t included in the arbitration criteria. This distinction makes certain types of information and analysis, which clubs may not want to see in a free agent package, extremely important to an agent’s arbitration brief.

For example, skills like bunting and advancing runners with outs won’t fetch top dollar on the free agent market. But arbitration is a whole different ballgame. Exhibits detailing how a player excelled at “little things” – that resulted in wins – help make your brief a winner. Demonstrate how he performed better than comparable arbitration eligibles in these areas, and it strengthens your case even further.

Gregor Blanco didn’t post big numbers in core statistical categories, but he delivered in many other ways that impacted the Giants outstanding season.

1) Blanco did not hit into a double play in 453 plate appearances. This had only been done seven times since 1950.

2) He advanced a runner from second base with nobody out in seven of eight plate appearances in this situation.

3) He drove in a runner from third base with less than two outs 10 times in 18 opportunities. Six of these RBI came in the seventh inning or later, seven contributed to wins and three made up the margin of victory in one-run games.

4) Blanco topped 25 steals and had 5 triples in fewer than 400 at-bats. Only one other Giants player had done that since 1912.

5) He executed five successful sacrifice bunts. The Giants went 4-1 in these games.

All those feats are impressive enough, but they also contributed to a World Championship team. Looking back, for arbitration, that bottom line is all that matters.

Four Ways to Build Value for Arbitration

The arbitration season is a crazy time of year for baseball agents and The Sports Resource. So before it gets into full swing, here are strategies for making your briefs a winner.

Building a brief starts with telling a compelling story in the player profile section. That makes matching the player against key comparables easier and more convincing.

The approaches below work for both the player profile and comparables sections, and will make powerful points that complement a brief's core elements. 

History, All-time Greats, and Rarities. While their achievements may not find their way onto SportsCenter, players record historic achievements throughout the regular season. For example, Antonio Bastardo’s 14.0 strikeouts per nine innings rate this season has been matched by just two lefthanders in Major League history: Billy Wagner and Aroldis Chapman. We’re not saying Bastardo is as good as Wagner (obviously), but linking his name to an all-time great is huge. And such feats carry value even when they don’t involve common statistics. 

Marginal Value adds Major Value. Players on teams that narrowly reach the postseason – such as the Orioles and Cardinals this year – help their teams generate $25-to-$50 million of additional revenue. That’s according to research by Vince Gennaro, a consultant for Major League teams and author of the book “Diamond Dollars”. Reaching the playoffs also impacts club revenues for up to five seasons. Demonstrate how your player made that two or three-game difference for his club (and his key comparables didn’t), and you score a huge plus. 

Ballpark Figures. Park factors can be a tremendous weapon in arbitration. Hitters in Safeco Field, or any of the five California stadiums, are ideal for park adjustments. The same goes for pitchers in Coors Field, U.S. Cellular Field and Fenway Park. Our last newsletter addressed this topic for free agency. The concept works differently in arbitration – because the criteria does not allow for projections – but it’s a great tactic for evaluating past performance. 

Advanced Metrics. Clubs have used win expectancies and leverage index against relievers in recent hearings. So why not do the same? Even if they don’t help your case, it pays to prepare information for rebuttal. WAR is a powerful tool as well, but works better at some positions than others. Although their explanations consume some presentation time, advanced metrics complement core numbers extremely well.

We Can Do That

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

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 Big Three for Pitchers

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

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.

Properly Valuing Hit Types

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.

In Defense of Win Totals

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.

The Truth about Strikeouts

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.

Caution: Falling Offense

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?

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

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.

The Power of Park Effects

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.

What Core Numbers Don’t Reveal

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

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.

A Better Metric for Strikeouts

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.