Sunday, January 29, 2017

Value Add Version 5.0 Corrects for Trips Per Trip, Domination Distortion

Version 3.0 of Value Add corrected slight variations to ensure the sum of their players' Value Adds equalled the offensive and defensive team ratings on If a team has an offensive rating of 108 and the sum of the players' Value Add indicates they only add up to a 106 offensive rating, then each player is adjusted upward precisely to account for the fact that they are doing something that is not showing up on the stat sheet. Likewise if the team is underperforming what their stats would calculate, each player is adjusted downward.

In January 2017, Value Add 5.0 calculated for two additional factors to adjust the 3.0 ratings:

1. Version 3.0 indicates the players Value Add in terms of how much each player impacts a team's trips up and down the court per 100 trips down the court. However, fans typically want to know how many points a player is worth to their team PER GAME. I have had callers tell me that even the great Ken Pomeroy cannot seem to explain the PER 100 TRIPS vs. PER GAME distinction in sound bites on the radio - as Jeff Sagarin's basketball rankings are much easier to understand as a "points per game" measurement.

We start by looking at to calculate that the average team during the tempo free era has had 67.2 trips down the court (possessions) per game - so the basic adjustment to get a POINTS PER GAME Value Add is to multiply their Version 3.0 Value by .672 to translate the final result to match up with Sagarin's rankings.

YearAve Trips
Average   67.2

2. The second factor adjusted by Version 5.0 is the distortion to the Value Add rating of players on great teams who sit on the bench when their teams are blowing out lesser opponents. These games are particularly damaging to a rating, because not only do the key players not get as many stats in those blowouts, but because they are often against teams with poor defenses, they receive a lower rating for those games due to going up against an inferior defense.

In going through the sixteen seasons, it appears the good players on the teams that win by the biggest margins lose enough Value Add to justify a multiplier of 1.3 to account for the extra minutes and possessions they could have had against the lesser early season opponents.

On the flip side the stars on poor teams often play almost all the minutes while getting blown out by the great teams, getting credit for scoring against the greatest defenses in the land even though points often come against the subs and even walk-ons for the great team. A review of the seasons indicates that the team that gets blow out by the biggest margins should have their Version 3.0 ratings multiplied by 0.695 to account for the easy points and other stats they get.

Each team gets a different multiplier, with the team winning by the second biggest margin getting a slightly smaller multiplier than the 1.305, all the way down to the players on the 351st team getting a 0.695.

However, we accomplish both items 1 and 2 in this piece by also multiplying those values by the .672 to account for trips per game, so the top victory margin team actually has their Version 3.0 Values multiplied by 0.877, and the team with the second biggest margin a slightly lower factor, making the actual factor for the 351st team 0.467.

Adjustment from v3.0 to v5.0    Domination Distortion    Multiplied by Trips/Game
Teams with biggest ave.victory margin     1.305    0.877
Average Team     1.000    0.672
Team with biggest average defeat margin     0.695    0.467

Some pure analytics people who attend MIT Sloan do not like any factor that is not purely mathmatically based, but I subscribe to the Bill James' approach that when observation gets you very close to an accurate mathematical equation, you administer it in a way that gets you as close as possible to the truth.

A player's Version 5.0 Value Add Ranking indicates how many points he can be worth to a team. However, the next question is how many points the player is worth over the replacement players of the actual team on which he plays.

When Version 5.0 was first calculated January 29, 2017, the average Value Add of the top player on each of the 351 teams was 5.13. The following is the average of each of top nine players (so the average 9th player of all 351 teams is 0.24.

TeamAve Value Add
Top Player5.13
2nd Player3.58
3rd Player2.69
4th Player2.02
5th Player1.46
6th Player1.01
7th Player (Subtract)0.68
8th Player0.42
9th Player0.24

This is the rating on which each player is ranked. The true value of a player to the actual team on which he played is usually lower, as it is approximately his Value Add minus the 7th best player on his team. So on a typical team:

The actual point value of the best player on a team is actually 5.13 MINUS the 7th best players total of 0.68 for a true value of 4.45.

The fact is that losing a player that is not one of the top six players is tiny and not worth adjusting the projected result of an upcoming game. Certainly there can be a starting point guard who has a low Value Add rating because he misses so many shots and turns the ball over so much that his Value Add is low, but in general it is very low.

For this reason, in the initial Version 5.0 Ratings on January 29, 2017, Gonzaga's Nigel Williams-Goss was the 20th most valuable player on Gonzaga's team at 9.21 points per game. He would in fact be worth 8.53 points per game to a typical team - so could turn a five point loss into a three- or four-point win, and that is the standard we use to measure his value against other teams.

However, the Gonzaga 2017 team is so loaded that Williams-Goss is actually only worth 4.91 points per game. Losing five points per game off your Sagarin rating is huge when you are competing for a national championship. When these figures were calculated, Gonzaga's Sagarin rating was 93.92 - second in the country - and subtracting 4.91 leaves a "Gonzaga without Williams-Goss" Sagarin Rating of 89.01 for 16th in the nation (Gonzaga/Sagarin 93.92 MINUS losing Williams-Goss 9.21 PLUS 4.45 for Gonzaga's 7th player equals 89.01.

Domino Effect, Not Baseball War

Why? Because his possessions and minutes are being used by players who are almost as good. Gonzaga's Josh Perkins has the best Value Add of any 7th man in the country at 4.30, so the domino effect from Williams-Perkins being out for a game to everyone down to Perkins getting more opportunities calculates as 9.21 (Williams-Goss Value Add) MINUS 4.91 (Perkins' Value Add) to equal 4.91 (actual point shift in the final score by Williams-Goss missing a game.

Many call me and try to figure out the Value Add of the backup player who will most likely replace the injured player, but unlike Baseball WAR where a specific player replaces another specific player both in the lineup and at a position in the field, the possessions are spread among several players and positions are even moved throughout the game to compensate. The one exception is when a great point guard is injured and the team lacks a decent backup point guard.

Louisville has the second best 7th man, and the following are the 40 teams with such a strong top seven that you need to subtract at least 1.70 from any of their player's Value Add ratings to get the true number of points the team will be hurt if the player is out.

RnkBest 7th Players 1/29/2017Teams w/ Deepest BenchSubtract from Value
1Josh Perkins^ 13Gonzaga4.30
2Anas Mahmoud^ 14Louisville4.10
3Justin Leon^ 23Florida3.51
4Jack Salt^ 33Virginia3.48
5Shaquille Morris^ 24Wichita St.3.44
6Nate Fowler^ 51Butler3.44
7Casey Benson^ 2Oregon3.03
8Terry Maston^ 31Baylor2.87
9Nate Britt^ 0North Carolina2.71
10Lamont West^ 15West Virginia2.71
11Carlton Bragg^ 15Kansas2.60
12Khalil Iverson^ 21Wisconsin2.60
13Muhammad-Ali Abdur-Rahkman^ 12Michigan2.49
14Mychal Mulder^ 11Kentucky2.45
15Katin Reinhardt^ 22Marquette2.43
16Ryan Cline^ 14Purdue2.33
17Temple Gibbs^ 2Notre Dame2.29
18Devontavius Payne^ 1East Tennessee St.2.27
19Eric Paschall^ 4Villanova2.27
20Jarquez Smith^ 23Florida St.2.26
21Niem Stevenson^ 10Texas Tech2.24
22Chance Comanche^ 21Arizona2.24
23Nathan Taphorn^ 32Northwestern2.15
24Jordan Murphy^ 3Minnesota2.14
25Alvin Ellis^ 3Michigan St.2.08
26Sedrick Barefield^ 2Utah2.08
27Shembari Phillips^ 25Tennessee2.06
28Trey Thompson^ 1Arkansas2.04
29Kamari Murphy^ 21Miami FL2.01
30Terrence Samuel^ 5Penn St.1.97
31Isaiah Zierden^ 21Creighton1.96
32Tre Scott^ 13Cincinnati1.94
33Drew Urquhart^ 25Vermont1.94
34Sam Miller^ 2Dayton1.92
35Paul Watson^ 3Fresno St.1.91
36Jarvis Garrett^ 1Rhode Island1.89
37Tyrique Jones^ 0Xavier1.89
38Carlbe Ervin^ 1Kansas St.1.75
39Horace Spencer^ 0Auburn1.70
40Jonah Mathews^ 2USC1.70

The following are the original top 25 players calculated using Version 5.0 based on the Louisville, Michigan State and Villanova wins on January 29, 2017. The entire 4000+ players were released at shortly thereafter:

1Josh Hart^ 313.03VillanovaBE6'5"Sr
2Frank Mason^ 012.35KansasB125'11"Sr
3Luke Kennard^ 511.98DukeACC6'6"So
4Monte Morris^ 1111.37Iowa St.B126'3"Sr
5Lauri Markkanen^ 1011.15ArizonaP127'0Fr
6Yante Maten^ 110.41GeorgiaSEC6'8"Jr
7Ethan Happ^ 2210.39WisconsinB106'10"So
8Jevon Carter^ 210.35West VirginiaB126'2"Jr
9Ben Lammers^ 4410.13Georgia TechACC6'10"Jr
10Marcus Marshall^ 110.01NevadaMWC6'3"Sr
11Alec Peters^ 259.79ValparaisoHorz6'9"Sr
12Markelle Fultz^ 209.65WashingtonP126'4"Fr
13Jaylen Adams^ 109.58St. BonaventureA106'2"Jr
14Jalen Brunson^ 19.49VillanovaBE6'2"So
15Caleb Swanigan^ 509.48PurdueB106'9"So
16Sindarius Thornwell^ 09.32South CarolinaSEC6'5"Sr
17TJ Williams^ 109.32NortheasternCAA6'3"Sr
18Donovan Mitchell^ 459.29LouisvilleACC6'3"So
19Lucas Woodhouse^ 349.26Stony BrookAE6'3"Sr
20Nigel Williams-Goss^ 59.21GonzagaWCC6'3"Jr
21Jeffrey Carroll^ 309.20Oklahoma St.B126'6"Jr
22Dennis Smith^ 49.15North Carolina St.ACC6'3"Fr
23Bonzie Colson^ 359.05Notre DameACC6'5"Jr
24Joe Chealey^ 139.05College of CharlestonCAA6'4"Jr
25Jock Landale^ 349.05Saint Mary'sWCC6'11"Jr

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