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.
Monday, September 20, 2010
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.
Tuesday, September 14, 2010
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.
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.
Monday, September 13, 2010
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.
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.
Tuesday, August 17, 2010
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.
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.
Tuesday, July 27, 2010
Keeping it Simple
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.
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.
Tuesday, June 15, 2010
Digging Deeper
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.
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.
Monday, June 7, 2010
Stepping Up
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.
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.
Wednesday, June 2, 2010
Cox and Torre Have Lived Parallel Lives
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.
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.
Thursday, May 20, 2010
Adjusted Double-Doubles
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.
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.
Wednesday, March 10, 2010
MIT Sports Analytics Conference
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.
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.
Tuesday, January 5, 2010
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.
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.
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