Box score stats vs. expected, by in-game situation
Posted: Thu Mar 26, 2015 9:59 pm
I had a lot of fun with this:
I've been working with a database of player in-game splits going back to 1996-97 that I pulled from NBA.com. It’s a big database.
I've been using it to try and figure out ways to analyze player mentality by game situation. Basically, I want to show how players change their approach by score. Do some players look to shoot more in comebacks? Do they pass more or less aggressively if the score is close? I think with this dataset I'm able measure how players change their games, and that feels exciting.
You can see some of my earlier attempts in an earlier thread: viewtopic.php?f=2&t=8847&start=15
In that thread, I came up with a measure I called Mentality that tells, as an example, how a player changes his shooting rate in comebacks. A Shooting Mentality of +5 says a player takes 5% more true shot attempts in comebacks than you would expect. (There are nuances in this, as I adjusted for league averages, but that essentially covers the old idea).
That approach had a couple of problems:
- First, it introduced a completely new stat that’s tricky to grasp. A +5 shooting mentality is interesting, but it doesn’t relate easily back to any other stat.
- Second, because it’s a ratio that I adjust to seasonal league averages, it difficult to get any sort of accurate career-spanning number. That makes it tricky to properly categorize players.
So, I’ve come up with a new approach with the same data. Lacking a better name, I’m calling this method "Productivity vs. Expected." It works for any box score stat. So, assists, for example become "Assists vs. Expected." The final number is not a ratio, it's assists. Which is easy.
You can see a sample of my data in my Tableau charts for comeback situations. A comeback situation is any game score where a player is behind or tied. I’ll pop down the main link, and then explain my formulas.
https://public.tableau.com/views/NBAPro ... _count=yes
The charts show how many more shots, assists, rebounds etc. a player has recorded in an in-game situation than you would expect vs. an average distribution of his numbers according to his minutes played in that situations.
For comebacks, the basic formula for Shots vs. Expected is this:
TrueShotAattemptsTSAbehind - TSAexpected
Where TSAexpected is: TSA * ((MPbehind / MP) * (League Adjustment * 0.01+1))
Where League Adjustment is: ((TSALeague / MPLeague) - (TSALeagueBehind / MPLeagueBehind )) / (TSALeague / MPLeague) * - 100
I still use a league adjustment because there are severe leaguewide trends in many box stats. This season, the NBA has seen 5.4% more shot attempts recorded in comebacks, vs overall, for instance.
The final measure tells the number of shots a player has taken above what you’d expect. The beauty of this method is that instead of a ratio the final unit is simply shots.
It tells me that in this season, JJ Reddick (of all people) leads the league in taking 38 more TSAs than expected in comebacks. Players like Kyle Lowry, Jamal Crawford and Mike Conley also rank highly -- Lowry is the guy who sparked my whole obsession with in-game splits.
Meanwhile, James Harden falls at the bottom of the chart having taken -57 fewer TSAs when trailing this season than you would expect. Also ranking near the bottom are Rudy Gay, Lou Williams and Gordon Hayward.
My tableau charts also include charts for:
- Shots: https://public.tableau.com/shared/5SCFW ... _count=yes
- Assists: https://public.tableau.com/shared/5YN49 ... _count=yes
- Rebounds: https://public.tableau.com/shared/C7MJ2 ... _count=yes
- Steals: https://public.tableau.com/shared/TWKZ3 ... _count=yes
- Turnovers: https://public.tableau.com/shared/H2DWK ... _count=yes and,
- Blocks:https://public.tableau.com/shared/MCQ3D ... _count=yes
The data goes back to the 1996-97 season. So, there’s a lot there to look at. You can also filter by team. I’ve found it interesting to look at team comeback approaches. Look at how the Grizzlies look off Z-Bo for Conley, for instance: https://public.tableau.com/shared/SJZ5C ... _count=yes
The same formulas works for any other counting stat (that’s in the NBA.com in-game splits database). I've got a few more that aren't in the Tableau.
The process also works for other in-game situations. I’m putting together a similar package for close score situations (+/- 5 points) that’ll follow this one, for instance.
Lastly, the final numbers can also be turned into a per game or per minute stat or used as a counting stat in and of itself.
It’s in this last use that I’ll finish with.
As a counting stat, career Productivity vs. Expected reveals players who have consistently become more involved in certain game situations. A player who’s recorded many years of positive Shots vs. Expected has a track record of being active in comebacks, for example. You would think of these players as those who look to their own offence in comebacks or who put their teams on their backs.
In turn, the career numbers reveal who’s excelled in the opposite situation. A player who’s less productive in comebacks vs. his overall numbers, for instance, is necessarily more productive with a lead. You could fairly call these players ‘frontrunners.’
And, this is where I’m having so much fun with this data:
This next Tableau set lists players against each other by accumulated career shots, assists, rebounds etc. vs expected. https://public.tableau.com/views/NBACar ... _count=yes
(The other tabs in this Tableau chart career numbers in each stat alongside per36 numbers. Be sure to check those out too.)
I love this chart.
As you can see, there’s really fun results here. Tim Duncan is a monster in shots vs. expected in comebacks, both overall and per36. Nash likewise is a monster in both shots and assists. (Duncan meanwhile is merely excellent in assists vs. expected.) These two guys have consistently put their teams on their backs.
Other guys who rank highly in both include Ray Allen, Dirk, Fisher and for better or for worse, Josh Smith.
Meanwhile, I’ve got really some interesting guys at the bottom of the career charts. McGrady, Iverson and Prince have poor comeback shot numbers, for instance.
Comparing the names at the top of the list to those at the bottom, I can’t help but think I’m on to something here.
It’s worth noting Lebron rates pretty poorly on Shots vs. Expected, though you can see he’s tended to record more assists in comebacks to make up for it.
Some other outliers:
- Andre Miller has easily recorded the fewest assists vs. expected.
- Mozgov is recording many more rebounds per36 than anyone with his number of total rebounds.
- Hibbert, meanwhile is down at the bottom of that chart, along with Lebron.
- Kobe and Iverson’s have recorded the least Steals vs. Expected. Lebron ranks poorly here too.
- Kidd recorded the least Turnovers vs. Expected, by far. Nash ranks near the top, which isn't a surprise considering his extra assists.
- And, for whatever reason Moutombo ranks poorly here on Blocks vs. Expected. It’s hard to know if that’s a fair figure, since he predates the start of my database. Something to work on.
Overall, I’m giggling with excitement over the potential of this data.
What do you guys think?
(Oh, and I’m aware there’s a problem with Glenn Robinson’s numbers)
I've been working with a database of player in-game splits going back to 1996-97 that I pulled from NBA.com. It’s a big database.
I've been using it to try and figure out ways to analyze player mentality by game situation. Basically, I want to show how players change their approach by score. Do some players look to shoot more in comebacks? Do they pass more or less aggressively if the score is close? I think with this dataset I'm able measure how players change their games, and that feels exciting.
You can see some of my earlier attempts in an earlier thread: viewtopic.php?f=2&t=8847&start=15
In that thread, I came up with a measure I called Mentality that tells, as an example, how a player changes his shooting rate in comebacks. A Shooting Mentality of +5 says a player takes 5% more true shot attempts in comebacks than you would expect. (There are nuances in this, as I adjusted for league averages, but that essentially covers the old idea).
That approach had a couple of problems:
- First, it introduced a completely new stat that’s tricky to grasp. A +5 shooting mentality is interesting, but it doesn’t relate easily back to any other stat.
- Second, because it’s a ratio that I adjust to seasonal league averages, it difficult to get any sort of accurate career-spanning number. That makes it tricky to properly categorize players.
So, I’ve come up with a new approach with the same data. Lacking a better name, I’m calling this method "Productivity vs. Expected." It works for any box score stat. So, assists, for example become "Assists vs. Expected." The final number is not a ratio, it's assists. Which is easy.
You can see a sample of my data in my Tableau charts for comeback situations. A comeback situation is any game score where a player is behind or tied. I’ll pop down the main link, and then explain my formulas.
https://public.tableau.com/views/NBAPro ... _count=yes
The charts show how many more shots, assists, rebounds etc. a player has recorded in an in-game situation than you would expect vs. an average distribution of his numbers according to his minutes played in that situations.
For comebacks, the basic formula for Shots vs. Expected is this:
TrueShotAattemptsTSAbehind - TSAexpected
Where TSAexpected is: TSA * ((MPbehind / MP) * (League Adjustment * 0.01+1))
Where League Adjustment is: ((TSALeague / MPLeague) - (TSALeagueBehind / MPLeagueBehind )) / (TSALeague / MPLeague) * - 100
I still use a league adjustment because there are severe leaguewide trends in many box stats. This season, the NBA has seen 5.4% more shot attempts recorded in comebacks, vs overall, for instance.
The final measure tells the number of shots a player has taken above what you’d expect. The beauty of this method is that instead of a ratio the final unit is simply shots.
It tells me that in this season, JJ Reddick (of all people) leads the league in taking 38 more TSAs than expected in comebacks. Players like Kyle Lowry, Jamal Crawford and Mike Conley also rank highly -- Lowry is the guy who sparked my whole obsession with in-game splits.
Meanwhile, James Harden falls at the bottom of the chart having taken -57 fewer TSAs when trailing this season than you would expect. Also ranking near the bottom are Rudy Gay, Lou Williams and Gordon Hayward.
My tableau charts also include charts for:
- Shots: https://public.tableau.com/shared/5SCFW ... _count=yes
- Assists: https://public.tableau.com/shared/5YN49 ... _count=yes
- Rebounds: https://public.tableau.com/shared/C7MJ2 ... _count=yes
- Steals: https://public.tableau.com/shared/TWKZ3 ... _count=yes
- Turnovers: https://public.tableau.com/shared/H2DWK ... _count=yes and,
- Blocks:https://public.tableau.com/shared/MCQ3D ... _count=yes
The data goes back to the 1996-97 season. So, there’s a lot there to look at. You can also filter by team. I’ve found it interesting to look at team comeback approaches. Look at how the Grizzlies look off Z-Bo for Conley, for instance: https://public.tableau.com/shared/SJZ5C ... _count=yes
The same formulas works for any other counting stat (that’s in the NBA.com in-game splits database). I've got a few more that aren't in the Tableau.
The process also works for other in-game situations. I’m putting together a similar package for close score situations (+/- 5 points) that’ll follow this one, for instance.
Lastly, the final numbers can also be turned into a per game or per minute stat or used as a counting stat in and of itself.
It’s in this last use that I’ll finish with.
As a counting stat, career Productivity vs. Expected reveals players who have consistently become more involved in certain game situations. A player who’s recorded many years of positive Shots vs. Expected has a track record of being active in comebacks, for example. You would think of these players as those who look to their own offence in comebacks or who put their teams on their backs.
In turn, the career numbers reveal who’s excelled in the opposite situation. A player who’s less productive in comebacks vs. his overall numbers, for instance, is necessarily more productive with a lead. You could fairly call these players ‘frontrunners.’
And, this is where I’m having so much fun with this data:
This next Tableau set lists players against each other by accumulated career shots, assists, rebounds etc. vs expected. https://public.tableau.com/views/NBACar ... _count=yes
(The other tabs in this Tableau chart career numbers in each stat alongside per36 numbers. Be sure to check those out too.)
I love this chart.
As you can see, there’s really fun results here. Tim Duncan is a monster in shots vs. expected in comebacks, both overall and per36. Nash likewise is a monster in both shots and assists. (Duncan meanwhile is merely excellent in assists vs. expected.) These two guys have consistently put their teams on their backs.
Other guys who rank highly in both include Ray Allen, Dirk, Fisher and for better or for worse, Josh Smith.
Meanwhile, I’ve got really some interesting guys at the bottom of the career charts. McGrady, Iverson and Prince have poor comeback shot numbers, for instance.
Comparing the names at the top of the list to those at the bottom, I can’t help but think I’m on to something here.
It’s worth noting Lebron rates pretty poorly on Shots vs. Expected, though you can see he’s tended to record more assists in comebacks to make up for it.
Some other outliers:
- Andre Miller has easily recorded the fewest assists vs. expected.
- Mozgov is recording many more rebounds per36 than anyone with his number of total rebounds.
- Hibbert, meanwhile is down at the bottom of that chart, along with Lebron.
- Kobe and Iverson’s have recorded the least Steals vs. Expected. Lebron ranks poorly here too.
- Kidd recorded the least Turnovers vs. Expected, by far. Nash ranks near the top, which isn't a surprise considering his extra assists.
- And, for whatever reason Moutombo ranks poorly here on Blocks vs. Expected. It’s hard to know if that’s a fair figure, since he predates the start of my database. Something to work on.
Overall, I’m giggling with excitement over the potential of this data.
What do you guys think?
(Oh, and I’m aware there’s a problem with Glenn Robinson’s numbers)