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.
Showing posts with label sports statistics. Show all posts
Showing posts with label sports statistics. Show all posts
Tuesday, September 20, 2011
Thursday, June 23, 2011
One Simple Test for Predicting NBA Success
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.
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.
Tuesday, June 14, 2011
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.
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.
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.
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.
Monday, June 6, 2011
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.
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.
Wednesday, June 1, 2011
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.
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 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.
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.
Friday, May 20, 2011
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.
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.
Thursday, March 31, 2011
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.
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.
Wednesday, March 16, 2011
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.
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.
Monday, September 20, 2010
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.
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.
Thursday, September 16, 2010
Opportunity and Statistics
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.
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.
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