https://www.postingandtoasting.com/2018/9/5/17800260/do-empty-stats-exist
Do ?empty stats? exist?
51
Enes Kanter needs to forget about DRE
By Drew Steele@ScooterToots Sep 5, 2018, 9:30am EDT
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Jeremy Brevard-USA TODAY Sports
It?s an important question to ask: do empty statistics exist in basketball? Short answer: no? per se. However, you?re reading this with the intention of me expanding on the topic, so I will continue. This concept of production not leading to value on the court or team wins is a fascinating one. Whether you want to frame the discussion harshly by saying a player has ?empty stats? or the more polite way of a player has ?good stats but on a bad team,? determining if box score statistics paint an accurate picture of a player?s value on the court is not only a discussion worth having, but also one that is ever present in online NBA communities, sports talk radio, and everything in between.
Every team has that guy who fans point to and say something along the lines of ?his numbers are empty and don?t contribute to winning.? DeMar DeRozan gets the brunt of this by a segment of Raptors fandom. Despite averaging 23.0 points, 3.9 rebounds, 5.2 assists, and being named an All-Star last season, Toronto was +9.9 with DeRozan off the floor compared to +7.2 when he was on the floor, an on/off differential ?2.7, per Basketball-Reference. For the Knicks, the empty stats target is zeroed in on Enes Kanter, a player whose on/off differential was ?2.3 as a bad Knicks was less bad with him off the floor.
Unfortunately for Kanter, he was the inspiration for this article. When you look at Kanter?s box score stats from last season, they look quite impressive: 14.1 points on 63.0 true shooting percentage, 11.0 rebounds, and a career-high 1.5 assists. His per-100 possession numbers are even more impressive, as he averaged 27.1 points and 21.1 rebounds. How could anyone say with a straight face that a player who averages an efficient double-double is not providing value, not contributing to winning, and is putting up empty statistics?
Well? Enes Kanter, despite averaging an efficient double-double, does not provide value, does not contribute to winning basketball, and puts up empty statistics? sort of.
?Empty stats? is a misleading term. The issue isn?t that a player?s box score statistics don?t have value ? they do and contribute to winning basketball games ? but rather a player is performing so poorly in other facets of the game that it is taking away the positives of putting up good basic box score statistics. This is where DRE enters the programs.
DRE is an acronym for ?Daily RAPM Estimate.? The metric was developed by Kevin Ferrigan back in 2015 and then updated in 2017. His goal was to track game-to-game performance fluctuations by determining what weights to place on specific box score metrics. Ferrigan?s methodology is somewhat of a raw, stripped-down version of Basketball-Reference?s box plus-minus. He ran a linear regression of per-100 possession basic box score stats against 14 seasons worth of multi-year RAPM.
Ferrigan reworked the regression?s explanitory variables a few years later and settled on the following: points, two-point attempts, three-point attempts, free throw attempts, offensive rebounds, defensive rebounds, assists, steals, blocks, turnovers and personal fouls (sound familiar?).
Do ?empty stats? exist?
51
Enes Kanter needs to forget about DRE
By Drew Steele@ScooterToots Sep 5, 2018, 9:30am EDT
SHARE
Jeremy Brevard-USA TODAY Sports
It?s an important question to ask: do empty statistics exist in basketball? Short answer: no? per se. However, you?re reading this with the intention of me expanding on the topic, so I will continue. This concept of production not leading to value on the court or team wins is a fascinating one. Whether you want to frame the discussion harshly by saying a player has ?empty stats? or the more polite way of a player has ?good stats but on a bad team,? determining if box score statistics paint an accurate picture of a player?s value on the court is not only a discussion worth having, but also one that is ever present in online NBA communities, sports talk radio, and everything in between.
Every team has that guy who fans point to and say something along the lines of ?his numbers are empty and don?t contribute to winning.? DeMar DeRozan gets the brunt of this by a segment of Raptors fandom. Despite averaging 23.0 points, 3.9 rebounds, 5.2 assists, and being named an All-Star last season, Toronto was +9.9 with DeRozan off the floor compared to +7.2 when he was on the floor, an on/off differential ?2.7, per Basketball-Reference. For the Knicks, the empty stats target is zeroed in on Enes Kanter, a player whose on/off differential was ?2.3 as a bad Knicks was less bad with him off the floor.
Unfortunately for Kanter, he was the inspiration for this article. When you look at Kanter?s box score stats from last season, they look quite impressive: 14.1 points on 63.0 true shooting percentage, 11.0 rebounds, and a career-high 1.5 assists. His per-100 possession numbers are even more impressive, as he averaged 27.1 points and 21.1 rebounds. How could anyone say with a straight face that a player who averages an efficient double-double is not providing value, not contributing to winning, and is putting up empty statistics?
Well? Enes Kanter, despite averaging an efficient double-double, does not provide value, does not contribute to winning basketball, and puts up empty statistics? sort of.
?Empty stats? is a misleading term. The issue isn?t that a player?s box score statistics don?t have value ? they do and contribute to winning basketball games ? but rather a player is performing so poorly in other facets of the game that it is taking away the positives of putting up good basic box score statistics. This is where DRE enters the programs.
DRE is an acronym for ?Daily RAPM Estimate.? The metric was developed by Kevin Ferrigan back in 2015 and then updated in 2017. His goal was to track game-to-game performance fluctuations by determining what weights to place on specific box score metrics. Ferrigan?s methodology is somewhat of a raw, stripped-down version of Basketball-Reference?s box plus-minus. He ran a linear regression of per-100 possession basic box score stats against 14 seasons worth of multi-year RAPM.
Ferrigan reworked the regression?s explanitory variables a few years later and settled on the following: points, two-point attempts, three-point attempts, free throw attempts, offensive rebounds, defensive rebounds, assists, steals, blocks, turnovers and personal fouls (sound familiar?).