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.
Thursday, May 20, 2010
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.
Monday, August 17, 2009
Getting Defensive about Offense
Believe it or not, one of biggest factors in any offensive comparison is defensive position. Let’s take two of the top contenders for the American League Most Valuable Player: Mark Teixeira and Joe Mauer.
Both have had extraordinary seasons. Teixeira has a .939 on-base plus slugging percentage, while Mauer has a 1.071 OPS. The gap between the players shrinks because Teixeira owns superior bulk, having contributed at that high offensive level in 523 plate appearances over 114 games, versus 405 plate appearances and 92 games for Mauer.
Defensive position makes a huge impact on this comparison. American League first basemen have averaged an .837 OPS this season. Teixeira tops that figure by 102 percentage points. AL catchers own a .726 OPS. Mauer exceeds the average by 345 percentage points. His production relative to position surpasses Teixeira by 243 points.
Any time a hitter posts big offensive numbers at catcher, second base, shortstop or center field, he provides immense value to his team. Why? Offense is less abundant at these defensive spots. So assuming the player fields his position adequately, his team gets superior offense where most teams get far less production.
Since team performance plays a role in MVP selections – and the Twins are a long shot to make the postseason – Mauer may not win the award. But his offensive value far exceeds Teixeira’s at the moment.
Both have had extraordinary seasons. Teixeira has a .939 on-base plus slugging percentage, while Mauer has a 1.071 OPS. The gap between the players shrinks because Teixeira owns superior bulk, having contributed at that high offensive level in 523 plate appearances over 114 games, versus 405 plate appearances and 92 games for Mauer.
Defensive position makes a huge impact on this comparison. American League first basemen have averaged an .837 OPS this season. Teixeira tops that figure by 102 percentage points. AL catchers own a .726 OPS. Mauer exceeds the average by 345 percentage points. His production relative to position surpasses Teixeira by 243 points.
Any time a hitter posts big offensive numbers at catcher, second base, shortstop or center field, he provides immense value to his team. Why? Offense is less abundant at these defensive spots. So assuming the player fields his position adequately, his team gets superior offense where most teams get far less production.
Since team performance plays a role in MVP selections – and the Twins are a long shot to make the postseason – Mauer may not win the award. But his offensive value far exceeds Teixeira’s at the moment.
Tuesday, August 11, 2009
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.
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.
Tuesday, July 7, 2009
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.
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.
Tuesday, June 23, 2009
Regression to the Mean: And What it Means for Agents
There’s a force more powerful than the Steelers defense or a monster slam from Shaquille O’Neal. It explains everything in sports from the sophomore jinx to unlikely postseason heroes to why slumps occur after hot starts.
“Regression to the mean” profoundly impacts sports statistics, yet you’ll never hear it mentioned on a game broadcast. Regression to the mean holds that as the sample size for a statistic increases, the amount the statistic varies within a group will decrease. In other words, the number of outrageously good and bad percentages will decrease as the season progresses and players/teams see more game action. They approach the mean, just another word for average.
Take the amazing 8.9 yards per carry average posted by Cowboys running back Felix Jones in 2008. He went down for the season after just 30 rushing attempts. Had he not gotten hurt, his yards per carry average would have dropped sharply. Not because defenses would focus on stopping him – Dallas had more dangerous offensive players – but due to regression to the mean.
Among NFL running backs that had at least 100 attempts during the 2008 season, none managed even 6 yards per carry. But many backs had 30-carry stretches when they approached Jones’ figure. The Cowboys rookie just happened to post his average over the course of a shortened season, before regression to the mean could rear its ugly head.
In fact, in the regular season’s final two games, the Giants’ Derrick Ward had a 9.7 yards per carry average in exactly 30 carries. For the season, Ward’s 5.6 average topped all backs with at least 100 rushing attempts. While an impressive feat, that’s a big drop-off from 8 or 9 yards. All caused by regression to the mean.
As an agent, it pays to understand this concept. Should one of your clients jump out to hot start, teams will tend to overvalue him. But he’s likely to see his statistics fall off. It works the other way too. When your player struggles early in the year, his numbers should improve, provided he continues to see game action.
Regression to the mean explains team performance as well. In 2007, four NFL teams won at least 13 games: the Patriots (16-0), Cowboys (13-3), Packers (13-3) and Colts (13-3). They combined for a sizzling .859 winning percentage and 55-9 record. This year, they had a combined 38-26 record and .594 winning percentage. And three of the four teams missed the playoffs! Injuries and other negative factors hit these teams hard, but so did regression to the mean.
In baseball, every postseason brings unlikely heroes. Why does this happen? The short postseason creates a small sample of games where average players can put up great stats before regression to the mean brings them down to earth. The same concept explains why some stars struggle in the postseason. That has little to do with choking – as some may claim – and everything to do with small sample sizes.
“Regression to the mean” profoundly impacts sports statistics, yet you’ll never hear it mentioned on a game broadcast. Regression to the mean holds that as the sample size for a statistic increases, the amount the statistic varies within a group will decrease. In other words, the number of outrageously good and bad percentages will decrease as the season progresses and players/teams see more game action. They approach the mean, just another word for average.
Take the amazing 8.9 yards per carry average posted by Cowboys running back Felix Jones in 2008. He went down for the season after just 30 rushing attempts. Had he not gotten hurt, his yards per carry average would have dropped sharply. Not because defenses would focus on stopping him – Dallas had more dangerous offensive players – but due to regression to the mean.
Among NFL running backs that had at least 100 attempts during the 2008 season, none managed even 6 yards per carry. But many backs had 30-carry stretches when they approached Jones’ figure. The Cowboys rookie just happened to post his average over the course of a shortened season, before regression to the mean could rear its ugly head.
In fact, in the regular season’s final two games, the Giants’ Derrick Ward had a 9.7 yards per carry average in exactly 30 carries. For the season, Ward’s 5.6 average topped all backs with at least 100 rushing attempts. While an impressive feat, that’s a big drop-off from 8 or 9 yards. All caused by regression to the mean.
As an agent, it pays to understand this concept. Should one of your clients jump out to hot start, teams will tend to overvalue him. But he’s likely to see his statistics fall off. It works the other way too. When your player struggles early in the year, his numbers should improve, provided he continues to see game action.
Regression to the mean explains team performance as well. In 2007, four NFL teams won at least 13 games: the Patriots (16-0), Cowboys (13-3), Packers (13-3) and Colts (13-3). They combined for a sizzling .859 winning percentage and 55-9 record. This year, they had a combined 38-26 record and .594 winning percentage. And three of the four teams missed the playoffs! Injuries and other negative factors hit these teams hard, but so did regression to the mean.
In baseball, every postseason brings unlikely heroes. Why does this happen? The short postseason creates a small sample of games where average players can put up great stats before regression to the mean brings them down to earth. The same concept explains why some stars struggle in the postseason. That has little to do with choking – as some may claim – and everything to do with small sample sizes.
Subscribe to:
Posts (Atom)
