Ok, I chose the title of this FanPost because it is 1) supposedly a good insult to a ref, 2) this post is related to fouls, and 3) there is some discrepancy in the data.
I was rather bored today and for whatever reason decided to try and look up advanced stats on fouls: how many shooting, how many personal, how many offensive, etc. I also wanted to see how many players give up the most points based on their fouls. Just because someone has 200 fouls does not mean they're much worse than someone who only has 125 fouls. Unfortunately (and I'm sure MFMP will prove me wrong) I couldn't find any data like this anywhere. I then thought about looking through play-by-play data and calculating that. Do you know long that would have taken? Well, I used my college degree to help me out, I wrote a program.
I copied a game's worth of play-by-play data into an Excel workbook and wrote a simple C# program to iterate through the rows and calculate a couple of things. After getting some kinks worked out, I then added a few more sheets for a few more games and checked to see if they added up. They did. So then I blindly did the rest of the season. I used play-by-play data from NBA.com (as compared to ESPN.com or Basketball-Reference.com) because of how they structured it. For instance, NBA.com shows a foul like "Monroe Foul: Shooting (1 PF) (2 FTA)" under the Detroit column. ESPN.com shows a foul like "Greg Monroe shooting foul (James Harden draws the foul )" also under the Detroit column. However, Basketball-Reference.com shows a foul like "Shooting foul by G. Monroe (drawn by J. Harden)" under the Opponent column. The easiest way to write the program was to use whatever provided data under the Detroit column, so Basketball-Reference.com was out. Then, because NBA.com provided how many free throws were to be shot, I decided that was the best data to use.
Here's what I found:
Player | TF | SF | SF% | PF | PF% | OF | OF% | A1 | F3 | PFFT | OFFT | FTM | FTA | FT% | Foul Count Discrepancy |
Bynum | 107 | 43 | 40.19% | 56 | 52.34% | 8 | 7.48% | 8 | 2 | 38 | 0 | 100 | 128 | 78.13% | -9 |
Calderon | 41 | 28 | 68.29% | 12 | 29.27% | 1 | 2.44% | 9 | 0 | 8 | 0 | 49 | 55 | 89.10% | 0 |
Drummond | 114 | 69 | 60.51% | 36 | 31.58% | 9 | 7.89% | 4 | 0 | 14 | 0 | 118 | 152 | 77.63% | -12 |
English | 39 | 22 | 56.41% | 14 | 35.90% | 3 | 7.69% | 6 | 2 | 16 | 0 | 45 | 60 | 75.00% | -2 |
Jerebko | 76 | 37 | 48.68% | 34 | 44.74% | 5 | 6.58% | 10 | 1 | 22 | 0 | 75 | 91 | 82.42% | -4 |
Knight | 142 | 49 | 34.51% | 78 | 54.93% | 15 | 10.56% | 11 | 1 | 28 | 0 | 94 | 124 | 75.81% | -7 |
Kravtsov | 29 | 16 | 55.17% | 7 | 24.14% | 6 | 20.69% | 4 | 0 | 4 | 0 | 23 | 34 | 67.65% | -2 |
Maggette | 41 | 14 | 34.15% | 21 | 51.22% | 6 | 14.63% | 3 | 2 | 8 | 0 | 27 | 37 | 72.97% | -1 |
Maxiell | 153 | 86 | 56.21% | 48 | 31.37% | 19 | 12.42% | 13 | 0 | 18 | 0 | 144 | 185 | 77.84% | -19 |
Middleton | 44 | 21 | 47.73% | 22 | 50.00% | 1 | 2.27% | 8 | 0 | 16 | 0 | 41 | 52 | 78.85% | -2 |
Monroe | 172 | 92 | 53.49% | 57 | 33.14% | 23 | 13.37% | 26 | 0 | 26 | 0 | 144 | 194 | 74.23% | -9 |
Singler | 194 | 102 | 52.58% | 77 | 39.69% | 15 | 7.73% | 19 | 2 | 20 | 0 | 161 | 211 | 76.30% | -19 |
Stuckey | 118 | 58 | 49.15% | 44 | 37.29% | 16 | 13.56% | 9 | 0 | 38 | 0 | 111 | 145 | 76.55% | -1 |
Villanueva | 87 | 60 | 68.97% | 24 | 27.59% | 3 | 3.45% | 12 | 0 | 16 | 0 | 82 | 126 | 65.08% | -6 |
Prince | 45 | 25 | 55.56% | 19 | 42.22% | 1 | 2.22% | 6 | 0 | 14 | 0 | 43 | 58 | 74.14% | 0 |
Daye | 39 | 25 | 64.10% | 10 | 25.64% | 4 | 10.26% | 6 | 2 | 6 | 0 | 47 | 56 | 83.93% | -4 |
TOTALS | 1441 | 747 | 51.84% | 559 | 38.79% | 135 | 9.37 | 154 | 12 | 292 | 0 | 1304 | 1708 | 76.35% | 97 |
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Hover over column headers for descriptions.
Now, let me explain the discrepancy. After running my program on all 78 games that have been played to date, I added up the SF, PF and OF and checked to see how close it was to what was in Stats.NBA.com. After seeing the discrepancies, I made a change to my program adding in a default catch (I have a switch/case for each player's name which is extrapolated from the cell's text value) just in case the name that was pulled wasn't one of the Pistons players. I put a breakpoint there, ran the program, and the breakpoint was never hit meaning each time the word "Foul" appeared in the text, it always pointed to a Pistons player (I wasn't looking in the Opponent's column). So somehow, NBA.com's play-by-play data does not match the counts in their database. (As I've been typing this, I was wondering the difference between the play-by-play on NBA.com and Stats.NBA.com and they are rather different..."Drummond S.FOUL (P1.T1)".) So the discrepancy in data seems to be that of NBA.com's data issues, not my program (or I hope that's the case).
Ok, so what can we take from this information? Well, the first thing (again, as long as the issue isn't my program) I see is that we don't ever seem to commit offensive fouls when we're in the bonus. Either that, or offensive fouls don't lead to the other teams free throws when they're in the bonus. I know offensive fouls don't count towards the penalty count, but not sure about after the bonus has already happened. The second thing I see is that Knight has only committed 15 offensive fouls. I honestly thought this count was higher. But since it isn't, that means 179 of Brandon's 194 total turnovers are from just plain crappy play.
I'm pretty sure there's other things in there that might be, or not be, surprising (such as the two people we joke about with the worst defense, Calderon and Villanueva, having the highest percentage of Shooting Fouls), but could this information be useful at all? When I looked at the team's fouls from 2000-01 through today, the lowest year for FPG was 2005-06 with 18.5. This year we currently average 19.7 which is third in that time span, just behind last year's of 19.6. One thing I would like to find out (and am looking into it) is finding out how long the Pistons have been in the Bonus compared to their opponents.
Well, let me know what you guys think of this data. Also, I'm thinking of keeping this program for next year and running it on every game for every team. I'm slowly working my way to those advanced stats.
UPDATE (4/16) - Further Breakdown
The data below is for all games played so far.
Sorry it took so long for the update. There were quite a few tweaks that I ended up making to my program and I found the fouls I wasn't counting before...Loose Ball and Clear Path Fouls. There were also a couple of instances where there might be a double technical or double personal and it wasn't showing up for that player as it wasn't in the same format. I also ran into a few instances where there would be a shooting/personal foul that resulted in free throws, a technical immediately after it, and the technical was shot first, messing up how I was calculating made free throws. I've changed all of that so it should be pretty damn accurate now.
Ok, so here's the breakdown of the different fouls. While a "And 1" shot is technically classified as a shooting foul, I separated them. Same with fouls on three point shots that didn't go in. Originally I had it to where I counted shooting fouls together, but had a column showing how many of them were And 1 and how many were fouls on 3 pointers. So total shooting fouls would be SF + A1 + 3PF. Also, not all Loose Ball fouls result in free throws (for those wondering why there didn't seem to be a trend between them). You can now also see how many Personal Fouls were intentional and how many were in the bonus. The last table of Personal Fouls are those that are run of the mill Personal Fouls. Let me know what you think of this more detailed data.
I may update this again later this week to include P36 counts, but spent too much time on it today just to get this.
Shooting Fouls
Player | Total Fouls | Shooting Fouls | % of Fouls | SFFTM | SFFTA | FT% |
Bynum | 128 | 36 | 28.13% | 58 | 72 | 80.56% |
Calderon | 41 | 19 | 46.34% | 32 | 38 | 84.21% |
Drummond | 143 | 71 | 49.65% | 108 | 142 | 76.06% |
English | 43 | 15 | 34.88% | 22 | 30 | 73.33% |
Jerebko | 88 | 28 | 31.82% | 49 | 56 | 87.50% |
Knight | 156 | 39 | 25.00% | 62 | 78 | 79.49% |
Kravtsov | 31 | 12 | 38.71% | 17 | 24 | 70.83% |
Maggette | 42 | 9 | 21.43% | 12 | 18 | 66.67% |
Maxiell | 173 | 73 | 42.20% | 115 | 146 | 78.77% |
Middleton | 53 | 17 | 32.08% | 23 | 34 | 67.65% |
Monroe | 189 | 68 | 35.98% | 108 | 136 | 79.41% |
Singler | 216 | 81 | 37.50% | 124 | 162 | 76.54% |
Stuckey | 128 | 52 | 40.63% | 78 | 104 | 75.00% |
Villanueva | 94 | 48 | 51.06% | 65 | 96 | 67.71% |
Daye | 42 | 17 | 40.48% | 27 | 34 | 79.41% |
Prince | 45 | 19 | 42.22% | 26 | 38 | 68.42% |
Personal Fouls in Bonus
Player | Total Fouls | Personal Fouls in Bonus | % of Fouls | PFBFTM | PFBFTA | FT% |
Bynum | 128 | 18 | 14.06% | 28 | 36 | 77.78% |
Calderon | 41 | 4 | 9.76% | 8 | 8 | 100.00% |
Drummond | 143 | 8 | 5.59% | 13 | 16 | 81.25% |
English | 43 | 8 | 18.60% | 12 | 16 | 75.00% |
Jerebko | 88 | 11 | 12.50% | 15 | 22 | 68.18% |
Knight | 156 | 14 | 8.97% | 22 | 28 | 78.57% |
Kravtsov | 31 | 2 | 6.45% | 3 | 4 | 75.00% |
Maggette | 42 | 4 | 9.52% | 6 | 8 | 75.00% |
Maxiell | 173 | 9 | 5.20% | 15 | 18 | 83.33% |
Middleton | 53 | 7 | 13.21% | 13 | 14 | 92.86% |
Monroe | 189 | 10 | 5.29% | 10 | 20 | 50.00% |
Singler | 216 | 8 | 3.70% | 12 | 16 | 75.00% |
Stuckey | 128 | 16 | 12.50% | 24 | 32 | 75.00% |
Villanueva | 94 | 8 | 8.51% | 10 | 16 | 62.50% |
Daye | 42 | 3 | 7.14% | 5 | 6 | 83.33% |
Prince | 45 | 7 | 15.56% | 13 | 14 | 92.86% |
Personal Fouls Taken (Intentional in the Bonus)
Player | Total Fouls | Personal Fouls Taken | % of Fouls | PFTFTM | PFTFTA | FT% |
Bynum | 128 | 4 | 3.13% | 5 | 8 | 62.50% |
Calderon | 41 | 1 | 2.44% | 0 | 2 | 0.00% |
Drummond | 143 | 1 | 0.70% | 0 | 2 | 0.00% |
Jerebko | 88 | 1 | 1.14% | 0 | 2 | 0.00% |
Knight | 156 | 1 | 0.64% | 0 | 2 | 0.00% |
Middleton | 53 | 1 | 1.89% | 2 | 2 | 100.00% |
Monroe | 189 | 4 | 2.12% | 4 | 8 | 50.00% |
Singler | 216 | 3 | 1.39% | 3 | 6 | 50.00% |
Stuckey | 128 | 5 | 3.91% | 5 | 10 | 50.00% |
Prince | 45 | 1 | 2.22% | 0 | 2 | 0.00% |
Clear Path Fouls
Player | Total Fouls | Clear Path Fouls | % of Fouls | CPFTM | CPFTA | FT% |
Bynum | 128 | 3 | 2.34% | 5 | 6 | 83.33% |
Knight | 156 | 1 | 0.64% | 1 | 2 | 50.00% |
Loose Ball Fouls
Player | Total Fouls | Loose Ball Fouls | % of Fouls | LBFTM | LBFTA | FT% |
Bynum | 128 | 5 | 3.91% | 1 | 2 | 50.00% |
Drummond | 143 | 12 | 8.39% | 4 | 4 | 100.00% |
Jerebko | 88 | 5 | 5.68% | 2 | 4 | 50.00% |
Knight | 156 | 5 | 3.21% | 2 | 4 | 50.00% |
Kravtsov | 31 | 2 | 6.45% | 2 | 2 | 100.00% |
Maxiell | 173 | 16 | 9.25% | 3 | 4 | 75.00% |
Middleton | 53 | 2 | 3.77% | 2 | 2 | 100.00% |
Monroe | 189 | 10 | 5.29% | 6 | 10 | 60.00% |
Singler | 216 | 19 | 8.80% | 2 | 4 | 50.00% |
Daye | 42 | 2 | 4.76% | 2 | 2 | 100.00% |
Flagrant Type 1 Fouls
Player | Total Fouls | Flagrant Type 1 Fouls | % of Fouls | F1FTM | F1FTA | FT% |
English | 43 | 1 | 2.33% | 2 | 2 | 100.00% |
Knight | 156 | 1 | 0.64% | 2 | 2 | 100.00% |
Maggette | 42 | 1 | 2.38% | 2 | 2 | 100.00% |
Maxiell | 173 | 2 | 1.16% | 2 | 4 | 50.00% |
Daye | 42 | 1 | 2.38% | 2 | 2 | 100.00% |
Flagrant Type 2 Fouls
Player | Total Fouls | Flagrant Type 1 Fouls | % of Fouls | F2FTM | F2FTA | FT% |
Bynum | 128 | 1 | 0.78% | 1 | 2 | 50.00% |
Villanueva | 94 | 1 | 1.06% | 2 | 2 | 100.00% |
Technical Fouls
Player | Total Fouls | Technical Fouls | % of Fouls | TFFTM | TFFTA | FT% |
Bynum | 128 | 1 | 0.78% | 1 | 1 | 100.00% |
Drummond | 143 | 3 | 2.10% | 2 | 3 | 66.67% |
Jerebko | 88 | 2 | 2.27% | 0 | 2 | 0.00% |
Monroe | 189 | 3 | 1.59% | 2 | 3 | 66.67% |
Stuckey | 128 | 2 | 1.56% | 2 | 2 | 100.00% |
Villanueva | 94 | 1 | 1.06% | 0 | 1 | 0.00% |
Abdenour | 1 | 1 | 100.00% | 1 | 1 | 100.00% |
Frank | 4 | 4 | 100.00% | 4 | 4 | 100.00% |
And 1 (HEY!) Fouls
Player | Total Fouls | And 1 Fouls | % of Fouls | A1FTM | A1FTA | FT% |
Bynum | 128 | 10 | 7.81% | 10 | 10 | 100.00% |
Calderon | 41 | 9 | 21.95% | 8 | 9 | 88.89% |
Drummond | 143 | 6 | 4.20% | 4 | 6 | 66.67% |
English | 43 | 7 | 16.28% | 6 | 7 | 85.71% |
Jerebko | 88 | 10 | 11.36% | 9 | 10 | 90.00% |
Knight | 156 | 13 | 8.33% | 11 | 13 | 84.62% |
Kravtsov | 31 | 4 | 12.90% | 1 | 4 | 25.00% |
Maggette | 42 | 3 | 7.14% | 3 | 3 | 100.00% |
Maxiell | 173 | 13 | 7.51% | 10 | 13 | 76.92% |
Middleton | 53 | 8 | 15.09% | 8 | 8 | 100.00% |
Monroe | 189 | 27 | 14.29% | 20 | 27 | 74.07% |
Singler | 216 | 20 | 9.26% | 16 | 20 | 80.00% |
Stuckey | 128 | 9 | 7.03% | 8 | 9 | 88.89% |
Villanueva | 94 | 12 | 12.77% | 5 | 12 | 41.67% |
Daye | 42 | 6 | 14.29% | 6 | 6 | 100.00% |
Prince | 45 | 6 | 13.33% | 4 | 6 | 66.67% |
3 Pointer Fouls (3 Shots)
Player | Total Fouls | 3 Pointer Fouls | % of Fouls | 3PFTM | 3PFTA | FT% |
Bynum | 128 | 2 | 1.56% | 3 | 6 | 50.00% |
English | 43 | 2 | 4.65% | 4 | 6 | 66.67% |
Jerebko | 88 | 1 | 1.14% | 3 | 3 | 100.00% |
Knight | 156 | 1 | 0.64% | 2 | 3 | 66.67% |
Maggette | 42 | 2 | 4.76% | 4 | 6 | 66.67% |
Singler | 216 | 2 | 0.93% | 4 | 6 | 66.67% |
Stuckey | 128 | 1 | 0.78% | 2 | 3 | 66.67% |
Personal Fouls
Player | Total Fouls | Personal Fouls | % of Fouls |
Bynum | 128 | 40 | 31.25% |
Calderon | 41 | 7 | 17.07% |
Drummond | 143 | 31 | 21.68% |
English | 43 | 7 | 16.28% |
Jerebko | 88 | 24 | 27.27% |
Knight | 156 | 66 | 42.31% |
Kravtsov | 31 | 5 | 16.13% |
Maggette | 42 | 17 | 40.48% |
Maxiell | 173 | 40 | 22.54% |
Middleton | 53 | 16 | 30.19% |
Monroe | 189 | 45 | 23.81% |
Singler | 216 | 67 | 31.02% |
Stuckey | 128 | 25 | 19.53% |
Villanueva | 94 | 16 | 17.02% |
Daye | 42 | 7 | 16.67% |
Prince | 45 | 11 | 24.44% |
Offensive Fouls
Player | Total Fouls | Offensive Fouls | % of Fouls |
Bynum | 128 | 8 | 6.25% |
Calderon | 41 | 1 | 2.44% |
Drummond | 143 | 11 | 7.69% |
English | 43 | 3 | 6.98% |
Jerebko | 88 | 6 | 6.82% |
Knight | 156 | 15 | 9.62% |
Kravtsov | 31 | 6 | 19.35% |
Maggette | 42 | 6 | 14.29% |
Maxiell | 173 | 19 | 10.98% |
Middleton | 53 | 2 | 3.77% |
Monroe | 189 | 22 | 11.64% |
Singler | 216 | 15 | 6.94% |
Stuckey | 128 | 17 | 13.28% |
Villanueva | 94 | 3 | 3.19% |
Daye | 42 | 4 | 9.52% |
Prince | 45 | 1 | 2.22% |