{"id":497,"date":"2011-04-15T12:02:58","date_gmt":"2011-04-15T17:02:58","guid":{"rendered":"http:\/\/godismyjudgeok.com\/DStats\/?p=497"},"modified":"2011-04-15T13:20:56","modified_gmt":"2011-04-15T18:20:56","slug":"nba-final-regular-season-bayesian-ratings","status":"publish","type":"post","link":"https:\/\/godismyjudgeok.com\/DStats\/2011\/nba-rankings\/nba-final-regular-season-bayesian-ratings\/","title":{"rendered":"NBA Final Regular Season Bayesian Ratings"},"content":{"rendered":"

There is a new page on this site<\/a>!\u00a0 I’m putting together an NBA Playoffs preview; this is the first of the data for that:<\/p>\n

Here is a table of playoff bound teams and their season-long adjusted efficiency ratings. Ratings are adjusted for location, pace (obviously, since these are per 100 possessions), opponent (recursively) and rest day situation. Previous is the rating of the team at the time of the trade deadline.<\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
Rank<\/strong><\/th>\nChg<\/strong><\/th>\nTeam<\/strong><\/th>\nOff Eff<\/strong><\/th>\nDef Eff<\/strong><\/th>\nEff. Margin<\/strong><\/th>\nChange<\/strong><\/th>\nPrev<\/strong><\/th>\nSoS<\/strong><\/th>\nPace<\/strong><\/th>\n<\/tr>\n
1<\/td>\n<\/td>\nMIA<\/strong><\/td>\n4.35<\/strong><\/td>\n-3.06<\/strong><\/td>\n7.41<\/strong><\/td>\n-0.63<\/td>\n8.04<\/td>\n-0.79<\/td>\n90.3<\/td>\n<\/tr>\n
2<\/td>\n3<\/td>\nCHI<\/strong><\/td>\n0.19<\/strong><\/td>\n-7.08<\/strong><\/td>\n7.27<\/strong><\/td>\n1.57<\/td>\n5.71<\/td>\n-0.73<\/td>\n89.7<\/td>\n<\/tr>\n
3<\/td>\n1<\/td>\nLAL<\/strong><\/td>\n4.01<\/strong><\/td>\n-2.64<\/strong><\/td>\n6.65<\/strong><\/td>\n0.12<\/td>\n6.52<\/td>\n-0.05<\/td>\n90.0<\/td>\n<\/tr>\n
4<\/td>\n-2<\/td>\nSAS<\/strong><\/td>\n4.13<\/strong><\/td>\n-1.84<\/strong><\/td>\n5.98<\/strong><\/td>\n-1.05<\/td>\n7.03<\/td>\n-0.22<\/td>\n91.6<\/td>\n<\/tr>\n
5<\/td>\n-2<\/td>\nBOS<\/strong><\/td>\n-0.96<\/strong><\/td>\n-6.41<\/strong><\/td>\n5.44<\/strong><\/td>\n-1.53<\/td>\n6.97<\/td>\n-0.46<\/td>\n89.4<\/td>\n<\/tr>\n
6<\/td>\n<\/td>\nORL<\/strong><\/td>\n0.87<\/strong><\/td>\n-4.49<\/strong><\/td>\n5.36<\/strong><\/td>\n-0.17<\/td>\n5.54<\/td>\n-0.54<\/td>\n90.7<\/td>\n<\/tr>\n
7<\/td>\n1<\/td>\nDEN<\/strong><\/td>\n4.96<\/strong><\/td>\n0.00<\/strong><\/td>\n4.96<\/strong><\/td>\n1.94<\/td>\n3.02<\/td>\n-0.04<\/td>\n95.1<\/td>\n<\/tr>\n
8<\/td>\n-1<\/td>\nDAL<\/strong><\/td>\n1.50<\/strong><\/td>\n-3.12<\/strong><\/td>\n4.63<\/strong><\/td>\n1.04<\/td>\n3.58<\/td>\n-0.07<\/td>\n90.6<\/td>\n<\/tr>\n
9<\/td>\n2<\/td>\nOKC<\/strong><\/td>\n3.95<\/strong><\/td>\n0.28<\/strong><\/td>\n3.67<\/strong><\/td>\n1.58<\/td>\n2.09<\/td>\n-0.33<\/td>\n91.9<\/td>\n<\/tr>\n
10<\/td>\n2<\/td>\nMEM<\/strong><\/td>\n0.23<\/strong><\/td>\n-2.47<\/strong><\/td>\n2.71<\/strong><\/td>\n0.86<\/td>\n1.85<\/td>\n0.21<\/td>\n91.0<\/td>\n<\/tr>\n
11<\/td>\n4<\/td>\nHOU<\/strong><\/td>\n3.52<\/strong><\/td>\n1.18<\/strong><\/td>\n2.33<\/strong><\/td>\n1.68<\/td>\n0.66<\/td>\n0.03<\/td>\n93.4<\/td>\n<\/tr>\n
12<\/td>\n1<\/td>\nPOR<\/strong><\/td>\n1.83<\/strong><\/td>\n-0.49<\/strong><\/td>\n2.32<\/strong><\/td>\n1.09<\/td>\n1.23<\/td>\n0.62<\/td>\n86.9<\/td>\n<\/tr>\n
13<\/td>\n-3<\/td>\nPHI<\/strong><\/td>\n-0.60<\/strong><\/td>\n-1.93<\/strong><\/td>\n1.34<\/strong><\/td>\n-0.87<\/td>\n2.20<\/td>\n-0.26<\/td>\n90.9<\/td>\n<\/tr>\n
14<\/td>\n-5<\/td>\nNOH<\/strong><\/td>\n-0.99<\/strong><\/td>\n-2.10<\/strong><\/td>\n1.11<\/strong><\/td>\n-1.28<\/td>\n2.40<\/td>\n0.11<\/td>\n88.7<\/td>\n<\/tr>\n
15<\/td>\n-1<\/td>\nNYK<\/strong><\/td>\n3.92<\/strong><\/td>\n2.92<\/strong><\/td>\n1.00<\/strong><\/td>\n0.21<\/td>\n0.79<\/td>\n0.20<\/td>\n95.3<\/td>\n<\/tr>\n
16<\/td>\n2<\/td>\nPHO<\/strong><\/td>\n2.65<\/strong><\/td>\n3.22<\/strong><\/td>\n-0.57<\/strong><\/td>\n-0.09<\/td>\n-0.48<\/td>\n0.33<\/td>\n93.7<\/td>\n<\/tr>\n
17<\/td>\n-1<\/td>\nATL<\/strong><\/td>\n-0.67<\/strong><\/td>\n0.33<\/strong><\/td>\n-1.01<\/strong><\/td>\n-1.45<\/td>\n0.44<\/td>\n-0.11<\/td>\n88.5<\/td>\n<\/tr>\n
18<\/td>\n2<\/td>\nMIL<\/strong><\/td>\n-6.60<\/strong><\/td>\n-5.22<\/strong><\/td>\n-1.39<\/strong><\/td>\n0.05<\/td>\n-1.44<\/td>\n-0.49<\/td>\n89.0<\/td>\n<\/tr>\n
19<\/td>\n-2<\/td>\nIND<\/strong><\/td>\n-2.12<\/strong><\/td>\n-0.72<\/strong><\/td>\n-1.40<\/strong><\/td>\n-1.10<\/td>\n-0.30<\/td>\n-0.20<\/td>\n94.5<\/td>\n<\/tr>\n
20<\/td>\n-1<\/td>\nUTA<\/strong><\/td>\n1.09<\/strong><\/td>\n2.79<\/strong><\/td>\n-1.70<\/strong><\/td>\n-1.04<\/td>\n-0.67<\/td>\n0.30<\/td>\n91.2<\/td>\n<\/tr>\n
21<\/td>\n<\/td>\nGSW<\/strong><\/td>\n1.75<\/strong><\/td>\n3.64<\/strong><\/td>\n-1.89<\/strong><\/td>\n0.95<\/td>\n-2.84<\/td>\n0.61<\/td>\n94.1<\/td>\n<\/tr>\n
22<\/td>\n1<\/td>\nLAC<\/strong><\/td>\n-2.03<\/strong><\/td>\n0.79<\/strong><\/td>\n-2.82<\/strong><\/td>\n0.34<\/td>\n-3.16<\/td>\n0.58<\/td>\n91.7<\/td>\n<\/tr>\n
23<\/td>\n1<\/td>\nDET<\/strong><\/td>\n0.82<\/strong><\/td>\n5.09<\/strong><\/td>\n-4.27<\/strong><\/td>\n0.13<\/td>\n-4.41<\/td>\n-0.27<\/td>\n88.6<\/td>\n<\/tr>\n
24<\/td>\n-2<\/td>\nCHA<\/strong><\/td>\n-3.77<\/strong><\/td>\n0.73<\/strong><\/td>\n-4.49<\/strong><\/td>\n-1.60<\/td>\n-2.89<\/td>\n-0.09<\/td>\n89.0<\/td>\n<\/tr>\n
25<\/td>\n1<\/td>\nSAC<\/strong><\/td>\n-4.06<\/strong><\/td>\n1.13<\/strong><\/td>\n-5.19<\/strong><\/td>\n0.59<\/td>\n-5.78<\/td>\n0.31<\/td>\n94.5<\/td>\n<\/tr>\n
26<\/td>\n1<\/td>\nMIN<\/strong><\/td>\n-2.97<\/strong><\/td>\n3.44<\/strong><\/td>\n-6.41<\/strong><\/td>\n-0.25<\/td>\n-6.16<\/td>\n0.49<\/td>\n96.0<\/td>\n<\/tr>\n
27<\/td>\n1<\/td>\nNJN<\/strong><\/td>\n-3.84<\/strong><\/td>\n2.87<\/strong><\/td>\n-6.71<\/strong><\/td>\n-0.14<\/td>\n-6.57<\/td>\n0.09<\/td>\n89.3<\/td>\n<\/tr>\n
28<\/td>\n-3<\/td>\nTOR<\/strong><\/td>\n-0.97<\/strong><\/td>\n5.76<\/strong><\/td>\n-6.73<\/strong><\/td>\n-0.95<\/td>\n-5.78<\/td>\n0.07<\/td>\n92.5<\/td>\n<\/tr>\n
29<\/td>\n<\/td>\nWAS<\/strong><\/td>\n-4.78<\/strong><\/td>\n3.16<\/strong><\/td>\n-7.94<\/strong><\/td>\n-0.86<\/td>\n-7.08<\/td>\n-0.14<\/td>\n93.4<\/td>\n<\/tr>\n
30<\/td>\n<\/td>\nCLE<\/strong><\/td>\n-5.43<\/strong><\/td>\n4.23<\/strong><\/td>\n-9.66<\/strong><\/td>\n0.85<\/td>\n-10.51<\/td>\n-0.06<\/td>\n92.8<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

That’s the basics, covered elsewhere. Next, let’s go Bayesian!
\n<\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
Rk<\/strong><\/th>\nTeam<\/strong><\/th>\nEff Dif.<\/strong><\/th>\nBayesian<\/strong><\/th>\nBayes OE<\/strong><\/th>\nBayes DE<\/strong><\/th>\nPace<\/strong><\/th>\nMax L8<\/strong><\/th>\nL18<\/strong><\/th>\nL62<\/strong><\/th>\nStdev<\/strong><\/th>\n<\/tr>\n
1<\/td>\nCHI<\/strong><\/td>\n7.27<\/td>\n7.14<\/strong><\/td>\n1.59<\/td>\n-4.71<\/td>\n89.3<\/td>\n7.42<\/td>\n7.86<\/td>\n7.86<\/td>\n12.23<\/td>\n<\/tr>\n
2<\/td>\nLAL<\/strong><\/td>\n6.65<\/td>\n5.39<\/strong><\/td>\n2.06<\/td>\n-2.69<\/td>\n90.0<\/td>\n6.89<\/td>\n6.89<\/td>\n6.89<\/td>\n12.19<\/td>\n<\/tr>\n
3<\/td>\nMIA<\/strong><\/td>\n7.41<\/td>\n6.42<\/strong><\/td>\n3.94<\/td>\n-1.72<\/td>\n90.0<\/td>\n6.42<\/td>\n6.42<\/td>\n7.86<\/td>\n12.98<\/td>\n<\/tr>\n
4<\/td>\nDEN<\/strong><\/td>\n4.96<\/td>\n5.18<\/strong><\/td>\n3.64<\/td>\n-0.93<\/td>\n94.9<\/td>\n5.41<\/td>\n5.41<\/td>\n5.41<\/td>\n11.66<\/td>\n<\/tr>\n
5<\/td>\nSAS<\/strong><\/td>\n5.98<\/td>\n4.70<\/strong><\/td>\n3.35<\/td>\n-0.79<\/td>\n91.3<\/td>\n4.92<\/td>\n5.28<\/td>\n6.95<\/td>\n12.25<\/td>\n<\/tr>\n
6<\/td>\nORL<\/strong><\/td>\n5.36<\/td>\n4.76<\/strong><\/td>\n0.46<\/td>\n-3.73<\/td>\n89.0<\/td>\n4.76<\/td>\n5.25<\/td>\n5.67<\/td>\n10.36<\/td>\n<\/tr>\n
7<\/td>\nBOS<\/strong><\/td>\n5.44<\/td>\n3.61<\/strong><\/td>\n-1.43<\/td>\n-4.62<\/td>\n88.8<\/td>\n4.38<\/td>\n4.71<\/td>\n8.74<\/td>\n11.41<\/td>\n<\/tr>\n
8<\/td>\nDAL<\/strong><\/td>\n4.63<\/td>\n4.62<\/strong><\/td>\n1.59<\/td>\n-2.48<\/td>\n91.8<\/td>\n4.62<\/td>\n4.62<\/td>\n5.75<\/td>\n11.82<\/td>\n<\/tr>\n
9<\/td>\nOKC<\/strong><\/td>\n3.67<\/td>\n4.32<\/strong><\/td>\n3.59<\/td>\n-0.22<\/td>\n91.7<\/td>\n4.59<\/td>\n4.59<\/td>\n4.59<\/td>\n12.16<\/td>\n<\/tr>\n
10<\/td>\nMEM<\/strong><\/td>\n2.71<\/td>\n3.01<\/strong><\/td>\n0.97<\/td>\n-1.69<\/td>\n90.2<\/td>\n3.68<\/td>\n3.68<\/td>\n3.68<\/td>\n12.00<\/td>\n<\/tr>\n
11<\/td>\nPOR<\/strong><\/td>\n2.32<\/td>\n2.63<\/strong><\/td>\n1.99<\/td>\n-0.33<\/td>\n86.6<\/td>\n3.33<\/td>\n3.36<\/td>\n3.36<\/td>\n13.21<\/td>\n<\/tr>\n
12<\/td>\nHOU<\/strong><\/td>\n2.33<\/td>\n2.72<\/strong><\/td>\n3.15<\/td>\n0.75<\/td>\n93.0<\/td>\n3.01<\/td>\n3.18<\/td>\n3.18<\/td>\n11.83<\/td>\n<\/tr>\n
13<\/td>\nPHI<\/strong><\/td>\n1.34<\/td>\n1.08<\/strong><\/td>\n-0.23<\/td>\n-1.18<\/td>\n90.4<\/td>\n2.75<\/td>\n2.75<\/td>\n3.03<\/td>\n12.03<\/td>\n<\/tr>\n
14<\/td>\nNOH<\/strong><\/td>\n1.11<\/td>\n-0.33<\/strong><\/td>\n-0.46<\/td>\n-0.17<\/td>\n92.7<\/td>\n1.15<\/td>\n1.55<\/td>\n4.18<\/td>\n12.05<\/td>\n<\/tr>\n
15<\/td>\nNYK<\/strong><\/td>\n1.00<\/td>\n0.92<\/strong><\/td>\n3.92<\/td>\n3.11<\/td>\n93.9<\/td>\n1.23<\/td>\n1.51<\/td>\n2.07<\/td>\n11.79<\/td>\n<\/tr>\n
16<\/td>\nPHO<\/strong><\/td>\n-0.57<\/td>\n-0.75<\/strong><\/td>\n1.55<\/td>\n2.21<\/td>\n93.5<\/td>\n-0.40<\/td>\n0.40<\/td>\n1.04<\/td>\n10.83<\/td>\n<\/tr>\n
17<\/td>\nATL<\/strong><\/td>\n-1.01<\/td>\n-2.34<\/strong><\/td>\n-1.12<\/td>\n0.95<\/td>\n88.5<\/td>\n-0.63<\/td>\n0.12<\/td>\n2.77<\/td>\n13.35<\/td>\n<\/tr>\n
18<\/td>\nGSW<\/strong><\/td>\n-1.89<\/td>\n-0.74<\/strong><\/td>\n1.38<\/td>\n2.03<\/td>\n94.0<\/td>\n-0.74<\/td>\n-0.74<\/td>\n-0.74<\/td>\n12.92<\/td>\n<\/tr>\n
19<\/td>\nMIL<\/strong><\/td>\n-1.39<\/td>\n-1.08<\/strong><\/td>\n-4.47<\/td>\n-3.52<\/td>\n89.1<\/td>\n-0.87<\/td>\n-0.87<\/td>\n0.02<\/td>\n12.02<\/td>\n<\/tr>\n
20<\/td>\nIND<\/strong><\/td>\n-1.40<\/td>\n-1.54<\/strong><\/td>\n-1.56<\/td>\n-0.20<\/td>\n93.9<\/td>\n-1.27<\/td>\n-0.95<\/td>\n1.35<\/td>\n11.64<\/td>\n<\/tr>\n
21<\/td>\nLAC<\/strong><\/td>\n-2.82<\/td>\n-2.37<\/strong><\/td>\n-1.82<\/td>\n0.27<\/td>\n92.7<\/td>\n-2.26<\/td>\n-1.62<\/td>\n-0.44<\/td>\n10.71<\/td>\n<\/tr>\n
22<\/td>\nUTA<\/strong><\/td>\n-1.70<\/td>\n-2.68<\/strong><\/td>\n0.51<\/td>\n2.87<\/td>\n94.7<\/td>\n-2.68<\/td>\n-2.11<\/td>\n4.37<\/td>\n11.34<\/td>\n<\/tr>\n
23<\/td>\nSAC<\/strong><\/td>\n-5.19<\/td>\n-3.64<\/strong><\/td>\n-2.43<\/td>\n0.77<\/td>\n95.4<\/td>\n-3.17<\/td>\n-3.17<\/td>\n-3.17<\/td>\n12.56<\/td>\n<\/tr>\n
24<\/td>\nDET<\/strong><\/td>\n-4.27<\/td>\n-3.27<\/strong><\/td>\n1.67<\/td>\n4.55<\/td>\n89.8<\/td>\n-3.21<\/td>\n-3.21<\/td>\n-2.95<\/td>\n10.19<\/td>\n<\/tr>\n
25<\/td>\nCHA<\/strong><\/td>\n-4.49<\/td>\n-4.25<\/strong><\/td>\n-2.16<\/td>\n1.58<\/td>\n88.5<\/td>\n-3.96<\/td>\n-3.39<\/td>\n-1.76<\/td>\n12.39<\/td>\n<\/tr>\n
26<\/td>\nMIN<\/strong><\/td>\n-6.41<\/td>\n-6.28<\/strong><\/td>\n-2.79<\/td>\n2.74<\/td>\n95.0<\/td>\n-4.98<\/td>\n-3.89<\/td>\n-3.43<\/td>\n12.31<\/td>\n<\/tr>\n
27<\/td>\nNJN<\/strong><\/td>\n-6.71<\/td>\n-6.28<\/strong><\/td>\n-2.43<\/td>\n3.11<\/td>\n90.0<\/td>\n-5.73<\/td>\n-4.93<\/td>\n-3.65<\/td>\n9.88<\/td>\n<\/tr>\n
28<\/td>\nTOR<\/strong><\/td>\n-6.73<\/td>\n-6.45<\/strong><\/td>\n-0.58<\/td>\n5.11<\/td>\n91.7<\/td>\n-6.21<\/td>\n-5.87<\/td>\n-2.15<\/td>\n11.29<\/td>\n<\/tr>\n
29<\/td>\nWAS<\/strong><\/td>\n-7.94<\/td>\n-6.89<\/strong><\/td>\n-3.48<\/td>\n2.59<\/td>\n93.5<\/td>\n-6.73<\/td>\n-6.73<\/td>\n-4.49<\/td>\n12.93<\/td>\n<\/tr>\n
30<\/td>\nCLE<\/strong><\/td>\n-9.66<\/td>\n-7.62<\/strong><\/td>\n-3.29<\/td>\n3.42<\/td>\n91.8<\/td>\n-7.62<\/td>\n-7.62<\/td>\n-6.86<\/td>\n12.33<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

I sorted the Bayesian table by the maximum rating in the last 18 games, to try to avoid a few teams’ (LAL) tanking at the end. The 18 was chosen to allow 5 or 6 games since the trade deadline for the ratings to stabilize somewhat with the new players.<\/p>\n

The standard deviation was calculated by taking the error for each of the last 30 games and running Stdev = Sqrt(average((Pred-Actual)^2)). Since this is a limited sample, the standard deviation for each team was regressed toward the average (12.0), using the prior being the overall distribution and the error for the standard deviation = 0.71*stdev\/sqrt(N), where N was 30 for the number of observations.<\/p>\n

Here is a Google Motion Chart showing how the Bayesian Ratings have progressed:<\/p>\n

<\/div>\n

EDIT: Bayesian Power Rating Spreadsheet and Data here, for the first time ever!<\/a><\/strong><\/p>\n","protected":false},"excerpt":{"rendered":"

There is a !\u00a0 I’m putting together an NBA Playoffs preview; this is the first of the data for that: Here is a table of playoff bound teams and their season-long adjusted efficiency ratings. Ratings are adjusted for location, pace (obviously, since these are per 100 possessions), opponent (recursively) and rest day situation. Previous is … [Read more…]<\/a><\/span><\/p>\n","protected":false},"author":3,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"ngg_post_thumbnail":0,"footnotes":""},"categories":[5,12],"tags":[21,57,54,37,27,22,24],"_links":{"self":[{"href":"https:\/\/godismyjudgeok.com\/DStats\/wp-json\/wp\/v2\/posts\/497"}],"collection":[{"href":"https:\/\/godismyjudgeok.com\/DStats\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/godismyjudgeok.com\/DStats\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/godismyjudgeok.com\/DStats\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/godismyjudgeok.com\/DStats\/wp-json\/wp\/v2\/comments?post=497"}],"version-history":[{"count":2,"href":"https:\/\/godismyjudgeok.com\/DStats\/wp-json\/wp\/v2\/posts\/497\/revisions"}],"predecessor-version":[{"id":503,"href":"https:\/\/godismyjudgeok.com\/DStats\/wp-json\/wp\/v2\/posts\/497\/revisions\/503"}],"wp:attachment":[{"href":"https:\/\/godismyjudgeok.com\/DStats\/wp-json\/wp\/v2\/media?parent=497"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/godismyjudgeok.com\/DStats\/wp-json\/wp\/v2\/categories?post=497"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/godismyjudgeok.com\/DStats\/wp-json\/wp\/v2\/tags?post=497"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}