How to Adjust KenPom Ratings Based on Injured Player's Value Add

UConn is a 3-point favorite against Georgetown tonight at www.kenpom.com, due to a calculation we estimate below this table. However, www.valueaddbasketball.com calculates that UConn is 4 points better when Bouknight plays and 4 points worse when he does not play (he has played 8 times and was injured for 8 of UConn's first 16 games). Since now Georgetown player makes the table, that is the only adjustment we would make to the expected score - Bouknight is expected to play tonight and therefore we adjust UConn to a 7-point favorite instead of a three point favorite.

Obviously in a one game sample they might win by three, or by more than seven, or lose, but this gives us a player adjustment for any player who is worth at least 4.0 points per game and has missed at least three games this season. If any other player not on this list misses a game, then simply go to www.valueaddbasketball.com and subtract his Value Add from his team's expected score to get an updated expected margin of victory, but for these players you improve their team projection by the "Playing" column or reduce their team projection by the "Not Playing" column.

Top 2% Players	Team	Playing	Not playing	Per Game	Rank	Played	Value Add	Rank
James Bouknight #2	Connecticut	4	-4	7.68	46	8 of 16	3.84	448
Tyrece Radford #23	Virginia Tech	2	-6	7.61	50	14 of 18	5.85	136
Sharife Cooper #2	Auburn	3	-4	7.35	55	12 of 23	3.87	443
Kevin McCullar #15	Texas Tech	3	-4	6.80	75	12 of 21	3.78	459
Iverson Molinar #1	Mississippi St.	1	-6	6.76	83	20 of 23	5.63	159
Michael Nuga #1	Kent St.	2	-5	6.75	84	12 of 18	4.5	320
Top 5% Players	Team	Playing	Not playing	Per Game	Rank	Games	Per Season	Rank
Matt Mitchell #11	San Diego St.	1	-6	6.61	92	17 of 20	5.51	170
Ian DuBose #11	Wake Forest	4	-2	6.53	100	6 of 16	2.42	909
Nijel Pack #24	Kansas St.	1	-5	6.48	105	19 of 23	5.4	179
Sasha Stefanovic #55	Purdue	1	-5	6.47	107	20 of 23	5.39	181
AJ Green #4	Northern Iowa	5	-1	6.23	118	3 of 20	0.93	1777
Obadiah Noel #11	UMass Lowell	1	-5	6.14	124	15 of 19	4.72	273
Jordan Bruner #2	Alabama	2	-4	5.92	142	14 of 23	3.7	479
JT Shumate #32	Toledo	2	-4	5.84	152	17 of 24	4.17	377
Devon Daniels #24	N.C. State	2	-4	5.60	174	12 of 19	3.5	529
Jason Preston #0	Ohio	1	-4	5.58	176	12 of 16	4.29	348
Shanquan Hemphill #4	Drake	1	-5	5.53	183	18 of 21	4.61	291
DeAndre Williams #12	Memphis	2	-3	5.50	188	11 of 18	3.44	548
KJ Walton #1	Ball St.	1	-5	5.48	190	15 of 18	4.57	306
Matt Bradley #20	California	2	-4	5.45	193	17 of 24	3.89	436
Keyontae Johnson #11	Florida	4	-1	5.38	203	4 of 17	1.25	1547
Ricky Lindo #4	George Washington	4	-1	5.38	204	3 of 13	1.25	1542
N'Faly Dante #1	Oregon	4	-2	5.37	206	6 of 18	1.79	1233
Top 10% Players	Team	Playing	Not playing	Per Game	Rank	Games	Per Season	Rank
Chad Baker #44	Duquesne	1	-4	5.27	217	11 of 14	4.05	399
Anthony Polite #2	Florida St.	1	-4	5.25	218	12 of 16	4.04	403
Marcus Shaver #0	Boise St.	1	-4	5.11	234	18 of 21	4.26	357
Keion Brooks #12	Kentucky	2	-3	4.97	261	12 of 21	2.76	759
DJ Funderburk #0	N.C. State	1	-4	4.90	269	15 of 19	3.77	466
Chris Smith #5	UCLA	3	-2	4.84	282	8 of 26	1.86	1200
Jalen Johnson #1	Duke	1	-3	4.82	285	13 of 18	3.44	549
Christian Guess #23	Samford	2	-3	4.73	295	9 of 16	2.63	820
Cam Mack #32	Prairie View A&M	1	-3	4.68	306	8 of 11	3.34	577
John Stansbury #5	Delaware St.	3	-1	4.64	314	3 of 12	1.16	1607
Mikael Jantunen #20	Utah	1	-4	4.63	317	15 of 19	3.56	514
Daejon Davis #1	Stanford	3	-2	4.62	318	10 of 23	2.01	1106
Justin Smith #0	Arkansas	1	-4	4.62	320	18 of 22	3.85	447
Scottie Lewis #23	Florida	1	-4	4.62	321	13 of 17	3.55	516
Jabari Walker #12	Colorado	1	-3	4.54	343	18 of 24	3.49	533
Johnny Juzang #3	UCLA	1	-4	4.48	352	17 of 21	3.73	475
Dallas Walton #13	Colorado	1	-3	4.46	354	18 of 24	3.43	553
Bo Hodges #1	Butler	3	-1	4.45	355	6 of 19	1.39	1459
John Meeks #12	Bucknell	2	-2	4.44	357	2 of 4	2.22	1001
Brevin Galloway #55	Charleston	3	-1	4.40	364	4 of 16	1.1	1656
Oscar Tshiebwe #34	West Virginia	2	-2	4.39	365	10 of 21	2.09	1072
Eric Williams #50	Oregon	1	-3	4.36	368	14 of 18	3.35	575
Joe Quintana #2	Loyola Marymount	1	-4	4.34	372	15 of 18	3.62	500
Jalen Gibbs #2	Mount St. Mary's	3	-1	4.32	381	6 of 18	1.44	1417
Geo Baker #0	Rutgers	1	-4	4.21	400	17 of 20	3.51	523
Elijah Childs #10	Bradley	1	-4	4.20	409	20 of 23	3.5	528
Charlie Moore #11	DePaul	1	-3	4.20	410	11 of 14	3.23	608
Jemarl Baker #3	Arizona	2	-2	4.20	411	12 of 23	2.21	1009
Other Players 4.0+	Team	Playing	Not	Per Game	Rank	Games	Per Season	Rank
Aljaz Kunc #4	Washington St.	1	-3	4.18	415	20 of 24	3.48	536
Jordan Cintron #20	Niagara	1	-3	4.18	416	15 of 18	3.48	538
Arkel Lamar #14	UMKC	3	-1	4.17	419	5 of 15	1.39	1458
Isaac Bonton #10	Washington St.	1	-3	4.08	436	19 of 24	3.14	628
CJ Fleming #25	Bellarmine	1	-3	4.04	445	13 of 16	3.37	572

Long Mathematical Explanation

For any mathematicians who want to understand the math, here is a basic rundown.

Value Add Basketball calculates each player's value above a replacement player to his team during the season. As an example, James Bouknight's Value Add tells us he has been worth almost 4 points a game - 3.84 - to UConn in their first 16 games.

Ken Pomeroy's rankings tell us how much better or worse a team is than average per 100 trips down the court. UConn's +17.43 means they would be an average team by just more than 17 points per 100 possessions, but since there are close to 70 possessions in an average game you can multiply the KenPom ranking by 0.7 and +17.43 x .7 = +12.20. UConn is 12.20 points per game better than the average team in a given game.

UConn's next opponent, Georgetown, has an +8.90 rating which is multiplied by .7 tells us Georgetown is 6.23 points better than the average team.

UConn's 12.20 minus Georgetown's 6.23 indicates UConn is almost exactly 6 points better than Georgetown, but since Georgetown will be the home team and Pomeroy calculates Georgetown's home court advantage is 3.1, UConn is a 3 point favorite in the game.

His actual formula's for determining the predicted margin of victory in each game likely have some tweaks, and if two teams are playing at a fast pace or slow pace then the margin would increase or shrink due to more or less than 70 possessions, but that is the basic idea when you reverse engineer.

The only variable this does not capture is if a key player is missing from one of the teams, or if a team's rating was held down because a player was missing for some games, but they are now actually better than their Pomeroy rating because he is back.

Thus the example of Bouknight, who conveniently played in exactly as many games as he missed during UConn's first 16 games. 

We can figure out how many points Bouknight was actually worth in the games he played through the following formula:

Value Add listed divided by Games Played by Player multiplied by Games Played by Team equals Actual Per Game Value Add when able to play.

Bouknight's rating at www.valueaddbasketball.com is 3.84, so we divide that by the eight games he played to date to get 0.48, then multiply that figure by the 16 games UConn has played to get 7.68.

This tells us that Bouknight has been worth and additional 7.68 points to UConn in the eight games he played (so turns a 4-point loss into a 4-point win). This means he is actually the 46th best player out of 4,183, so just misses the top one percent of all players. The reason the Value listed in the database is 3.84 is that in the eight UConn games when he was out injured, he was worth 0.00 points, so over the course of the season he has improved UConn 3.84 points per game UConn plays. He calculates as the 448th best player in the country, but if he had missed not games he was on pace to be the 46th best player in the country.

The next best player held back by injury is Tyrece Radford of Virginia Tech, whose 5.85 Value Add Rating would actually be 7.61 for the games in which he played.

We will go a little deeper into the math below this table, but 

Virginia Tech is projected to beat Georgia Tech by 2, so if Radford missed the game we subtract 6 points (5.85) and Virginia Tech is a 4 point underdog.

However, you also adjust up for a team that is at full strength, but the math is a little trickier. Below are all they key players who have missed at least three games, with their actual per game Value Add listed first, and then the listed Value Add next. You need to subtract the second figure from the first to see how much better than the kenpom rating the team is with the player now on the court.

For Bouknight, 7.68 minus 3.84 = 3.84, so UConn is actually four points better than their kenpom rating with him on the court and instead of a 3 point favorite against Georgetown they become a 7 point favorite at full strength. 

For Radford, 7.61 minus 5.85 = 1.76, so Virginia Tech is actually 2 points better than their kenpom rating with Radford back. Instead of a 2-point favorite Virginia Tech is a 4-point favorite against Georgia Tech.

The following are the only players whose injuries have made a substantial difference to factor in their adjusted kenpom rating. In looking at a match-up you need to see if the opponent likewise has a key player who has missed games. Neither of their opponents, Georgetown or Georgia Tech, has a player on the list below and therefore we can make the adjustment based on Bouknight and Radford being on the court.