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.
Monday, August 17, 2009
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.
Thursday, May 28, 2009
A Better Metric for Strikeouts
Even with sports analytics impacting baseball more every season, opportunities still exist to upgrade some mainstream statistics.
High strikeout totals grab media and fan attention, despite research showing that strikeouts are only slightly less damaging to offensive production than other outs. Nonetheless, since they get perceived as a negative, we should at least evaluate them in the proper context.
Ryan Howard (54), David Wright (50), Adam Dunn (50) and Prince Fielder (48) rank third through sixth in NL strikeouts (through May 27). But their strikeout rates – the percentage of plate appearances that end in a strikeout – rank much better. Howard drops from third in total strikeouts to fifth in strikeout rate. Wright falls from fourth to ninth, and Dunn goes from a tie for fourth with Wright to 10th. Fielder falls all the way from sixth to 13th. All these players get lots of plate appearances, which makes them look worse at making contact than is actually the case.
Strikeout rate also makes a great stat for showing which players shine at making contact. Miguel Tejada leads the NL with a 5.6 percent strikeout rate. Carlos Lee (7.9) and Albert Pujols (7.9) place fifth and sixth. By the way, this measure is superior to the more commonly used at-bats per strikeouts, which penalizes hitters who draw more walks than other players.
Strikeout rate also works as a better tool for evaluating pitchers than the more commonly used strikeouts per nine innings. For example, Zack Greinke (9.7) and Joba Chamberlain (9.1) have comparable strikeouts per nine innings figures. But Greinke (28.6) owns a far superior strikeout rate than Chamberlain (22.8). Why? Chamberlain has faced more batters per inning because he has allowed hits and walks more often than Greinke. This gives him more chances to log strikeouts each inning. But why should Greinke’s pitching effectiveness hurt him in this statistical category? If strikeout rate gets used, this isn’t a problem.
High strikeout totals grab media and fan attention, despite research showing that strikeouts are only slightly less damaging to offensive production than other outs. Nonetheless, since they get perceived as a negative, we should at least evaluate them in the proper context.
Ryan Howard (54), David Wright (50), Adam Dunn (50) and Prince Fielder (48) rank third through sixth in NL strikeouts (through May 27). But their strikeout rates – the percentage of plate appearances that end in a strikeout – rank much better. Howard drops from third in total strikeouts to fifth in strikeout rate. Wright falls from fourth to ninth, and Dunn goes from a tie for fourth with Wright to 10th. Fielder falls all the way from sixth to 13th. All these players get lots of plate appearances, which makes them look worse at making contact than is actually the case.
Strikeout rate also makes a great stat for showing which players shine at making contact. Miguel Tejada leads the NL with a 5.6 percent strikeout rate. Carlos Lee (7.9) and Albert Pujols (7.9) place fifth and sixth. By the way, this measure is superior to the more commonly used at-bats per strikeouts, which penalizes hitters who draw more walks than other players.
Strikeout rate also works as a better tool for evaluating pitchers than the more commonly used strikeouts per nine innings. For example, Zack Greinke (9.7) and Joba Chamberlain (9.1) have comparable strikeouts per nine innings figures. But Greinke (28.6) owns a far superior strikeout rate than Chamberlain (22.8). Why? Chamberlain has faced more batters per inning because he has allowed hits and walks more often than Greinke. This gives him more chances to log strikeouts each inning. But why should Greinke’s pitching effectiveness hurt him in this statistical category? If strikeout rate gets used, this isn’t a problem.
Friday, May 22, 2009
The LeBron Stoppers
In the coming years, Eastern Conference foes will be searching for ways to slow down the Cavs and LeBron James. Defenders effective against them will have tremendous value. Which five-man player combinations excelled at doing this in 2008-09?
The units in the chart had the best defensive efficiency against the Cavaliers with James on the court this season. Defensive efficiency is the number of points allowed divided by defensive possessions times 100.
This list includes the five-man combinations that posted a defensive efficiency below 95 while on the court versus Cleveland and James. To put that figure in perspective, the league had an average defensive efficiency near 108. The average against the Cavs with James playing was 115.1. Among the 71 units that faced them for at least 10 minutes this season, only these 16 posted a defensive efficiency under 95.
The red ink shows the player likely to have guarded James from each unit. In some cases, teams may have zoned or rotated defenders on James.
Units (Team) Def. Eff.
1 Brown-Hamilton-McDyess-Prince-Stuckey (Detroit) 79.4
2 Artest-Battier-Brooks-Yao-Scola (Houston) 80.5
3 Ford-Granger-Hibbert-Jack-Murphy (Indiana) 82.2
4 Deng-Hinrich-Nocioni-Rose-Tyrus Thomas (Chicago) 82.4
5 Brand-Iguodala-Miller-Williams-Young (Philadelphia) 82.6
6 Blake-Fernandez-Outlaw-Przybilla-Roy (Portland) 84.0
7 Iverson-AJohnson-Prince-Stuckey-Wallace (Detroit) 84.8
8 Hawes-Jackson-Martin-Nocioni-Thompson (Sacramento) 86.2
9 Howard-Lee-Lewis-Nelson-Turkoglu (Orlando) 87.1
10 Anthony-Chalmers-Haslem-Marion-Wade (Miami) 88.0
11 Brown-Hamilton-Iverson-Prince-Wallace (Detroit) 88.9
12 Azubuike-Biedrins-Ellis-Jackson-Morrow (Golden State) 89.3
13 Alston-Howard-Lee-Lewis-Turkoglu (Orlando) 90.5
14 Bibby-Horford-Johnson-Smith-Williams (Atlanta) 91.4
15 Deng-Gooden-Rose-Sefolosha-Tyrus Thomas (Chicago) 92.6
16 Dalembert-Evans-Green-Iguodala-Miller (Philadelphia) 93.3
Detroit had three of the top 11 defensive units versus James and the Cavs. The only player in common to all three groups was Tayshaun Prince, who guarded James himself. The Magic had the ninth and 13th place units, which could loom large in the Eastern Conference Finals. The only different between their two groups was at point guard with Rafer Alston taking over for the injured Jameer Nelson to join their other four starters. The Sixers and Bulls also had two units apiece on the list.
While these combinations excelled defensively against Cleveland with James on the court, they may not have limited his scoring. But they slowed down the other four players enough to succeed.
The units in the chart had the best defensive efficiency against the Cavaliers with James on the court this season. Defensive efficiency is the number of points allowed divided by defensive possessions times 100.
This list includes the five-man combinations that posted a defensive efficiency below 95 while on the court versus Cleveland and James. To put that figure in perspective, the league had an average defensive efficiency near 108. The average against the Cavs with James playing was 115.1. Among the 71 units that faced them for at least 10 minutes this season, only these 16 posted a defensive efficiency under 95.
The red ink shows the player likely to have guarded James from each unit. In some cases, teams may have zoned or rotated defenders on James.
Units (Team) Def. Eff.
1 Brown-Hamilton-McDyess-Prince-Stuckey (Detroit) 79.4
2 Artest-Battier-Brooks-Yao-Scola (Houston) 80.5
3 Ford-Granger-Hibbert-Jack-Murphy (Indiana) 82.2
4 Deng-Hinrich-Nocioni-Rose-Tyrus Thomas (Chicago) 82.4
5 Brand-Iguodala-Miller-Williams-Young (Philadelphia) 82.6
6 Blake-Fernandez-Outlaw-Przybilla-Roy (Portland) 84.0
7 Iverson-AJohnson-Prince-Stuckey-Wallace (Detroit) 84.8
8 Hawes-Jackson-Martin-Nocioni-Thompson (Sacramento) 86.2
9 Howard-Lee-Lewis-Nelson-Turkoglu (Orlando) 87.1
10 Anthony-Chalmers-Haslem-Marion-Wade (Miami) 88.0
11 Brown-Hamilton-Iverson-Prince-Wallace (Detroit) 88.9
12 Azubuike-Biedrins-Ellis-Jackson-Morrow (Golden State) 89.3
13 Alston-Howard-Lee-Lewis-Turkoglu (Orlando) 90.5
14 Bibby-Horford-Johnson-Smith-Williams (Atlanta) 91.4
15 Deng-Gooden-Rose-Sefolosha-Tyrus Thomas (Chicago) 92.6
16 Dalembert-Evans-Green-Iguodala-Miller (Philadelphia) 93.3
Detroit had three of the top 11 defensive units versus James and the Cavs. The only player in common to all three groups was Tayshaun Prince, who guarded James himself. The Magic had the ninth and 13th place units, which could loom large in the Eastern Conference Finals. The only different between their two groups was at point guard with Rafer Alston taking over for the injured Jameer Nelson to join their other four starters. The Sixers and Bulls also had two units apiece on the list.
While these combinations excelled defensively against Cleveland with James on the court, they may not have limited his scoring. But they slowed down the other four players enough to succeed.
Thursday, May 14, 2009
Opportunity and the NBA
When the Hawks managed a stop in their series versus the Hawks, there was Anderson Varejao battling to keep the ball alive any way possible. He didn’t always grab the board, but it deflated Atlanta when he did. All that work on defense, only to have to start with a fresh shot clock.
Watching players like Varejao and Chris Andersen impact their teams makes you wonder how many players like this are out there looking for opportunities: guys that rebound, defend, hustle and manage to score some without getting plays called for them.
One thing our research has revealed is that teams will always find top scorers. That’s not always true with rebounders. According to the numbers, getting an opportunity is much tougher for these players.
This season, 128 of the 329 NBA players that saw 500 minutes of action averaged 20 points per 48 minutes. Meanwhile, 105 of those 329 averaged 10 rebounds per 48 minutes. What’s interesting is the scorers averaged 2188 minutes played, 567 more minutes than the rebounders (1621). While just 15 of these scorers saw less than 20 minutes per game, 42 of the rebounders failed to get that much court time.
This effect gets even more extreme if we make the groups more selective. The league had 45 players that saw 500 or more minutes average 25 points per 48 minutes. A near equal number of players – 49 of them – had 13 boards per 48 minutes. This group of scorers averaged 2378 minutes played compared to 1628 for the rebounders. In other words, the extra rebounds did nothing to generate more playing time. They averaged only 7 more minutes per contest than the previous 10-rebound group. But the new scorers group averaged 190 more minutes.
Here’s the amazing thing: none of the 25 points per 48 minutes scorers failed to see less than 20 minutes per game. Lou Williams had the lowest figure at 23.7 minutes per game. The rebounders group had 15 players that saw less than 20 minutes per game.
So the message is clear: If you score – even as a bench player – some team will get you playing time. Rebounders have a tougher time. While quality scorers certainly impact the game more than quality board men, the gap isn’t large enough to explain this type of discrepancy.
A look back at the 2003-04 season produced comparable results. This time, the list of underutilized rebounders (13 rebounds per 48 minutes in less than 20 minutes per game) included two interesting names. The first, David West, later became an All-Star. Chris Andersen was the other. Not all the other players emerged – some have since left the game – but there are more Andersens and Varejaos out there. Is there a team that will give them a chance?
Watching players like Varejao and Chris Andersen impact their teams makes you wonder how many players like this are out there looking for opportunities: guys that rebound, defend, hustle and manage to score some without getting plays called for them.
One thing our research has revealed is that teams will always find top scorers. That’s not always true with rebounders. According to the numbers, getting an opportunity is much tougher for these players.
This season, 128 of the 329 NBA players that saw 500 minutes of action averaged 20 points per 48 minutes. Meanwhile, 105 of those 329 averaged 10 rebounds per 48 minutes. What’s interesting is the scorers averaged 2188 minutes played, 567 more minutes than the rebounders (1621). While just 15 of these scorers saw less than 20 minutes per game, 42 of the rebounders failed to get that much court time.
This effect gets even more extreme if we make the groups more selective. The league had 45 players that saw 500 or more minutes average 25 points per 48 minutes. A near equal number of players – 49 of them – had 13 boards per 48 minutes. This group of scorers averaged 2378 minutes played compared to 1628 for the rebounders. In other words, the extra rebounds did nothing to generate more playing time. They averaged only 7 more minutes per contest than the previous 10-rebound group. But the new scorers group averaged 190 more minutes.
Here’s the amazing thing: none of the 25 points per 48 minutes scorers failed to see less than 20 minutes per game. Lou Williams had the lowest figure at 23.7 minutes per game. The rebounders group had 15 players that saw less than 20 minutes per game.
So the message is clear: If you score – even as a bench player – some team will get you playing time. Rebounders have a tougher time. While quality scorers certainly impact the game more than quality board men, the gap isn’t large enough to explain this type of discrepancy.
A look back at the 2003-04 season produced comparable results. This time, the list of underutilized rebounders (13 rebounds per 48 minutes in less than 20 minutes per game) included two interesting names. The first, David West, later became an All-Star. Chris Andersen was the other. Not all the other players emerged – some have since left the game – but there are more Andersens and Varejaos out there. Is there a team that will give them a chance?
Sunday, March 29, 2009
10 Hot Topics from the MIT Sports Analytics Conference
For the second consecutive year, I attended the MIT Sports Analytics Conference on March 7. The event drew a large crowd of sports insiders, researchers, media and students. Among all the great information coming from the speakers and panelists, these were my top 10 highlights:
1. Dean Oliver of the Denver Nuggets, a member of the Basketball Analytics panel, pointed out that the NBA teams actively involved in analytics are now residing in the upper part of the standings.
2. Mike Zarren, the Celtics Assistant Executive Director of Basketball Operations, believes that the ability to communicate what numbers mean is as important as the statistics themselves.
3. Rockets GM Daryl Morey explained trading the #8 pick of the 2006 draft (which became Rudy Gay) for Shane Battier, drawing a parallel from the financial world. “You have to take risks. In a game where one of 30 teams wins [a championship], you can’t just try to beat the market index.”
4. Mark Cuban stated that chemistry is important for all businesses. In this respect, other businesses aren’t all that different from basketball.
5. Cuban estimates that one win is worth a half million dollars in revenue. He added that the most profitable type of club is one in rebuilding mode, with low salaries and a low win total.
6. Most fans, media and sports insiders feel strongly that players who hit a shot are more likely to connect the next time they shoot. How often during March Madness did announcers state that a premier shooter could start to go off after nailing a three-pointer? But John Huizinga, a college professor as well as Yao Ming’s agent, presented compelling evidence against such a theory. He showed that players are more likely to miss their next shot after sinking one.
7. The Baseball Analytics panel talked about how new metrics for evaluating fielding have made an impact on the game. John Dewan of Baseball Info Solutions pointed out that the difference between baseball’s best defensive team in 2008 (the Phillies) and the worst (the Royals) amounted to 130 runs. The offensive gap between the top run-scoring club (Texas) and the lowest (San Diego) was 260 runs. David Pinto followed that by explaining how defense turned the fortunes of the Devil Rays pitching staff in one year.
8. Shiraz Rehman, the Diamondbacks’ Director of Baseball Operations, spoke about the approach to valuing players. “The understanding of replacement value is becoming more apparent. What do we have to spend to get x value over replacement level?”
9. Speaking as a panelist on the “Value of Icon Players,” Celtics star Ray Allen fielded a question about individuals and team performance. He believes basketball players have to be a little cocky and crazy. He said that “if you do it the right way, individualism will make the team better.” He added that “having a great teammate is the best thing in sports.”
10. Despite the economic downturn, Tim Romani of ICON Venue Group sees stadium naming rights as a great way to activate a brand. He sighted the 02 Arena in London as a great example of what’s possible.
1. Dean Oliver of the Denver Nuggets, a member of the Basketball Analytics panel, pointed out that the NBA teams actively involved in analytics are now residing in the upper part of the standings.
2. Mike Zarren, the Celtics Assistant Executive Director of Basketball Operations, believes that the ability to communicate what numbers mean is as important as the statistics themselves.
3. Rockets GM Daryl Morey explained trading the #8 pick of the 2006 draft (which became Rudy Gay) for Shane Battier, drawing a parallel from the financial world. “You have to take risks. In a game where one of 30 teams wins [a championship], you can’t just try to beat the market index.”
4. Mark Cuban stated that chemistry is important for all businesses. In this respect, other businesses aren’t all that different from basketball.
5. Cuban estimates that one win is worth a half million dollars in revenue. He added that the most profitable type of club is one in rebuilding mode, with low salaries and a low win total.
6. Most fans, media and sports insiders feel strongly that players who hit a shot are more likely to connect the next time they shoot. How often during March Madness did announcers state that a premier shooter could start to go off after nailing a three-pointer? But John Huizinga, a college professor as well as Yao Ming’s agent, presented compelling evidence against such a theory. He showed that players are more likely to miss their next shot after sinking one.
7. The Baseball Analytics panel talked about how new metrics for evaluating fielding have made an impact on the game. John Dewan of Baseball Info Solutions pointed out that the difference between baseball’s best defensive team in 2008 (the Phillies) and the worst (the Royals) amounted to 130 runs. The offensive gap between the top run-scoring club (Texas) and the lowest (San Diego) was 260 runs. David Pinto followed that by explaining how defense turned the fortunes of the Devil Rays pitching staff in one year.
8. Shiraz Rehman, the Diamondbacks’ Director of Baseball Operations, spoke about the approach to valuing players. “The understanding of replacement value is becoming more apparent. What do we have to spend to get x value over replacement level?”
9. Speaking as a panelist on the “Value of Icon Players,” Celtics star Ray Allen fielded a question about individuals and team performance. He believes basketball players have to be a little cocky and crazy. He said that “if you do it the right way, individualism will make the team better.” He added that “having a great teammate is the best thing in sports.”
10. Despite the economic downturn, Tim Romani of ICON Venue Group sees stadium naming rights as a great way to activate a brand. He sighted the 02 Arena in London as a great example of what’s possible.
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