Five Reasons to Activate Your Sponsorships with Statistics

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

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

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

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

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

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

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.