Top 25 returning defensive players - and how to calculate them

Since I posted the offensive ratings of the 2500+ returning players based on the % of points they added to their teams, I’ve been asked why I didn’t also calculate the defensive % of points good players take away from their opponents.

The fact that so few defensive stats are kept is cumbersome for someone who lives number crunching professionally.

Ironically, this is the topic of a talk I will be giving to a couple of colleges, “Winning With Political Microtargeting and Sports Sabermatrics.” The fact is that everywhere in my professional work from City Council Campaigns to a couple of successful Presidential campaigns, I have used Microtargeting to calculate every message and delivery method (door-to-door, mail, TV), and how many votes a dollar spent on each will get FOR my candidate (offense) or dissuade people from voting for our opponent (defense). Likewise in basketball we keep offensive stats every time the team scores or fails to score, BUT ironically we only keep stats of 22% of the defensive contributions. To illustrate, below are the different ways a basketball possession can end, and how likely each is to happen per 100 possessions. (Parenthesis mean there is a stat that should be kept, but is not):

Possession ends with	#	Pts	Off credit/blame	Def credit/blame
3pts made	9	27	3PM (split credit if AST)	(PtsAllowed)
2pt made	26	52	FGM (split credit if AST)	(PtsAllowed)
FT made	13	19	FTM	(PtsAllowed)
Steal	9	0	TO	STL
Turnover other than steal	11	0	TO	(TOF)
blocked shot, def reb	3	0	FGA	2/3rd BLK, 1/3 REB
other missed 2, def reb	14	0	FGA	2/3rd (STOP), 1/3 REB
missed 3 pt, def reb	11	0	FGA	2/3rd (STOP), 1/3 REB
missed FT, def reb	4	0	FTA	2/3rd (STOP), 1/3 REB
100 possessions ended (2pts during possession))	100	100	credit/blame for 100%	credit/blame for 22%

(See note at bottom on why shooting percentages look higher than they are at first glance)

Please don't use this as an excuse to comment on politics, but comparing what is tracked is enlightning for me.

When Moneyball came out in 2003, the top Bush campaign guys went crazy over it. They loved the Sabermatrics behind it, and pretty soon they had built the most incredible Microtargeting model ever, calculating that the biggest bang for their campaign dollar would be getting specific people in specific Ohio localities who were predisposed to Bush to vote after sitting out the 2000 campaign. The fact that we don’t keep track of a player’s points allowed (PtsAllowed), forced turnovers other than steals (TOF) or defensive stops (STOP) would be like the Bush campaign saying, “I really don't want to calculate how many people will vote AGAINST Kerry, only how many will vote FOR Bush," or the Obama team deciding during their incredible Microtargeting of Virginia four years later that they really didn’t want to know why young Virginians might vote AGAINST McCain.

We simply have to do our best to calculate defense to have a whole picture of a player's overall statistical value to a team.

Here is what we can calculate from the 22% of individual defensive stats we do keep track of:

An average team blocks 9.2% of opponent’s 2-pointers takes away 2% of their points – double their blocks and they take away 4%. An average team steals the ball 9% of the time which takes away 9% of the opposition points. Double it and they take away 18% of the points. And finally a team that grabs the average 67.3% of opponents’ missed shots takes away 11% of their opponents points, so an individual player who gets one-fifth of that (13.5%) takes away just over 2% while he is on the floor. Here is the breakdown:

Adjust to ave. 1 pt. per trip	Pts+-	kenpom	Convert from Pomeroy
BLK	-2	9.2%	0.217
STL	-9	9.4%	0.957
REB (Defensive only)	-11	67.3%	0.163
No individual stat for 30 STOPS or TOF	-30
48 trips scored 100 points	52

To understand the Pts+-, consider that the AVERAGE possession results in just over 1.0 points. In the 100 possession in the first table, the team allowed points on 48 of them, but it was a total of 100 points scored (9 treys, 26 other field goals, and 19 free throws on the 13 times they finished with a made free throw). Since you expect 1 point allowed per trip, if you instead give up a 3-point shot then you are penalized for a+2 (3 is 2 points more than average), while a regular field goal is +1, etc., but a defensive stop is -1 since they didn’t get their one-point average that trip.

I’ve put in a converter so that you can multiply the Blk% in Pomeroy by .217 to determine what % of points a player is erasing from the opponents score with his blocked shots, steals and defensive rebounds.

However, that still leaves us way short of a Defensive Efficiency Rating (DRtg) that would match the precise Offensive Efficiency Rating (ORat) developed by Dean Oliver in 2003 and incorporated into www.kenpom.com that same year.

Introducing TeamDef72%

While we lack the INDIVIDUAL defensive stats we need, we do have them at the team level. Basically starting with Pomeroy’s Team Defensive Rating, which is the number of Points Allowed per possession, and taking out the influence of the teams above or below average defensive rebounding, steals and blocks, and we are left with one number that sums up a team’s Stops, Points Allowed and Turnovers Force. Here are the best and worst:

1. Florida State 94.4
2. Texas 94.9
3. San Diego State 95.2
4. North Carolina 95.9
5. Louisville 96.1
6. Ohio State 96.1
7. Duke 96.2
8. Utah State 96.4
9. Kansas 96.6
10. Purdue 96.9
61. Marquette 103.3 (42nd of 73 BCS teams)
343. Longwood 126.0
344. MD Baltimore County 126.1
345. Chicago State 127.3

What this figure does is to give every player on a team one-fifth of the Stops, Points Allowed and Turnovers Forced while he is on the court, which gives him this figure. We see that when we add that all together, even after adjusting for competition as built into Pomeroy’s defensive rating, MU was below average for a BCS school in all the things you do on defense that aren’t recorded.

With 72% of their rating taken care of, we can now add back in each player’s blocks, steals and defensive rebounds.

Once I ran the numbers (and I really have no idea where everyone will finish until I sort by Value), Jared Sullinger calculated as the best returning defensive player in the country. To walk through his numbers:

1. Ohio State’s “TeamDef72%” stat for all the things we don’t measure was a 96.1, meaning they were the 6th best team at all the defensive things you do for which we do not have a stat. We start by giving Sullinger that number as one of the five Buckeyes on the court making that happen.
2. Sullinger just missed being the top defensive rebounder in any BCS conference, and his 26.3% of rebounds grabbed after a miss can be multiplied by the factor on the table (.163)to convert this Pomeroy figure to save us some calculations in knowing his rebounding lower's opponents' scoring by 4.3%, so his defensive rating is lowered to 91.8.
3. He is not a great shotblocker, only rejecting 2% of opponents two pointers, which when multiplied by the .217 on the converter tells us his shotblocking lowers opponents' scoring by 0.4% for a total for 91.4.
4. Finally, he does get some steals, enough to erase another 1.9% of opponents scoring. For steals, we use the Stl% that actually appears on Pomeroy's page.
5. This gives Sullinger an 89.5 Defensive Efficiency Rating, the flip side of the ORtg developed by Oliver in 2003. Not as precise as the ORtg because we are dividing up a lot of the team activity equally, but the same basis.
6. You may notice that Bernard James of Florida State was actually a more effective defender when on the floor, with a DRtg of 86.3, meaning teams are only likely to score 86.3 points per trip per possession with him on the floor. Just ask Notre Dame about James after he played 21 minutes despite almost passing out sick, blocking 3 shots and grabbing 9 rebounds in that game to help to hold ND to 31% shooting from the floor. However, like with Kemba Walker in the blog on offense, it was more valuable for Sullinger to be on the floor for 78.9% of the minutes than for James to play 52.7% of the minutes.
7. Once we multiply by minutes played, we see that over the course of the season Sullinger reduced the number of points scored by Ohio State opponents by -7.3%, the best total in the country, while James was just a little behind at -6.6% in 3rd place.

As will come as no surprise to all of us who watched last year, Marquette did not have the same kind of production on defense as they did on offense. Jae Crowder calculates as easily the best returning offensive and defensive player for Marquette, but he is the 24th best in the country on offense while the 160th best defensive player of the 2500+ returning.

The whole list to come, but here is how the Top 25 nationally calculate, followed by Marquette's returning players.

Def Rank ret	Fname	Lname	Feet	Inches	Team	Team Def Rating	DefR erase	Blk erase	Stl erase	DRtg	%Min	Value Sub BCS	Value Sub Low D1
1	Jared	Sullinger	6	9	Ohio St.	96.1	4.3	0.4	1.9	89.5	78.9	-7.3
2	John	Henson	6	10	North Carolina	95.9	4.1	2.5	1.1	88.1	66.6	-7.1
3	Bernard	James	6	10	Florida St.	94.4	3.0	2.9	2.2	86.3	52.7	-6.6
4	Tony	Mitchell	6	6	Alabama	97.1	2.6	0.8	3.0	90.7	78.3	-6.3
5	Mason	Plumlee	6	10	Duke	96.2	3.9	1.3	1.9	89.1	64.1	-6.2
6	Terrence	Jones	6	8	Kentucky	97.8	3.7	1.3	2.0	90.9	78.4	-6.2
7	Aaron	Craft	6	2	Ohio St.	96.1	1.6	0.1	3.9	90.5	73.8	-6.1
8	JaMychal	Green	6	8	Alabama	97.1	2.9	1.8	3.0	89.5	63.6	-5.9
9	Peyton	Siva	5	11	Louisville	96.1	1.8	0.2	3.9	90.2	68.4	-5.9
10	Deividas	Dulkys	6	5	Florida St.	94.4	1.4	0.5	2.8	89.8	60.7	-5.5
11	Michael	Snaer	6	5	Florida St.	94.4	1.3	0.2	1.7	91.2	71.2	-5.4
12	Tyler	Zeller	7	0	North Carolina	95.9	2.5	0.9	1.3	91.1	70.1	-5.4
13	Samme	Givens	6	5	Drexel	97.9	4.3	0.7	1.3	91.6	73.9	-5.3
14	Draymond	Green	6	6	Michigan St.	100.2	3.9	0.9	3.3	92.1	74.4	-5.0
15	Brady	Jardine	6	7	Utah St.	96.4	4.2	1.2	1.4	89.6	53.2	-4.9
16	William	Buford	6	5	Ohio St.	96.1	1.9	0.2	1.4	92.6	78.6	-4.9
17	Harrison	Barnes	6	8	North Carolina	95.9	2.1	0.3	1.3	92.1	73.2	-4.9
18	Trevor	Releford	6	1	Alabama	97.1	1.6	0.1	3.0	92.5	77.7	-4.9
19	Herb	Pope	6	8	Seton Hall	97.4	3.3	1.0	1.6	91.4	65.1	-4.8
20	Chase	Tapley	6	2	San Diego St.	95.2	1.3	0.2	3.0	90.7	58.2	-4.7
21	Dexter	Strickland	6	3	North Carolina	95.9	1.3	0.0	2.4	92.2	67.3	-4.5
22	Yancy	Gates	6	9	Cincinnati	98.5	3.4	1.1	1.7	92.2	67.2	-4.4
23	Chris	Smith	6	2	Louisville	96.1	2.2	0.1	1.9	91.9	63.8	-4.4
24	Brockeith	Pane	6	1	Utah St.	96.4	1.7	0.1	1.9	92.7	70.1	-4.3
25	Alex	Oriakhi	6	9	Connecticut	97.8	3.0	1.2	0.8	92.8	71.4	-4.3
160	Jae	Crowder	6	6	Marquette	103.0	3.0	0.7	2.6	96.6	68.8	-1.5
320	Chris	Otule	6	11	Marquette	103.0	2.2	1.9	1.0	97.9	43.8	-0.4
345	Vander	Blue	6	4	Marquette	103.0	1.9	0.3	2.6	98.2	47.3	-0.3
428	Darius	Johnson-Odom	6	2	Marquette	103.0	1.6	0.2	1.5	99.7	74.7		-6.7
495	Jamil	Wilson	6	7	Marquette (2010 Oregon)	106.8	2.4	0.7	1.5	102.1	78.4		-5.2
584	Junior	Cadougan	6	1	Marquette	103.0	1.4	0.1	1.5	100.0	47.9		-4.2
1003	Davante	Gardner	6	8	Marquette	103.0	2.4	0.4	0.8	99.4	19.9		-1.8
1943	David	Singleton	6	3	Marquette (2010 High Point)	116.7	2.0	0.2	3.6	110.9	78.4

NOTE FROM TOP TABLE: At first glance, the shooting percentages seem to be way too high when looking at this table, however that is because it only shows plays on which the possession ends. While there are more made shots than misses here, the fact is DURING some possessions teams may have shot 3 or 4 times and kept grabbing offensive rebounds, etc., so there are many more FGM than just those that end a possession with a defensive rebound.