Summarize an elo Object
# S3 method for elo.run summary(object, ...) # S3 method for elo.glm summary(object, ...) # S3 method for elo.markovchain summary(object, ...) # S3 method for elo.colley summary(object, ...) # S3 method for elo.winpct summary(object, ...)
| object | An object to summarize. |
|---|---|
| ... | Other arguments |
A summary of object.
summary(elo.run(score(points.Home, points.Visitor) ~ team.Home + team.Visitor, data = tournament, k = 20)) #> #> An object of class 'summary.elo.run', containing information on 8 teams and 56 matches. #> #> Mean Square Error: 0.2195 #> AUC: 0.6304 #> Favored Teams vs. Actual Wins: #> Actual #> Favored 0 0.5 1 #> TRUE 6 1 16 #> (tie) 2 1 9 #> FALSE 8 3 10 summary(elo.glm(score(points.Home, points.Visitor) ~ team.Home + team.Visitor, data = tournament)) #> Warning: non-integer #successes in a binomial glm! #> #> Call: #> stats::glm(formula = wins.A ~ . - 1, family = family, data = dat.qr, #> weights = wts, subset = NULL, na.action = stats::na.pass) #> #> Deviance Residuals: #> Min 1Q Median 3Q Max #> -1.9887 -0.7133 0.4083 0.6533 2.0595 #> #> Coefficients: #> Estimate Std. Error z value Pr(>|z|) #> home.field 0.9643 0.3564 2.706 0.00681 ** #> `Athletic Armadillos` 1.4280 0.8969 1.592 0.11135 #> `Blundering Baboons` -0.8949 0.8666 -1.033 0.30177 #> `Cunning Cats` 0.5132 0.8336 0.616 0.53813 #> `Defense-less Dogs` -1.5293 0.9410 -1.625 0.10411 #> `Elegant Emus` 0.3414 0.8292 0.412 0.68050 #> `Fabulous Frogs` 0.8640 0.8498 1.017 0.30930 #> `Gallivanting Gorillas` 0.5132 0.8336 0.616 0.53813 #> --- #> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 #> #> (Dispersion parameter for binomial family taken to be 1) #> #> Null deviance: 70.701 on 56 degrees of freedom #> Residual deviance: 48.842 on 48 degrees of freedom #> AIC: 72.953 #> #> Number of Fisher Scoring iterations: 5 #> #> Mean Square Error: 0.1453 #> AUC: 0.8411 #> Favored Teams vs. Actual Wins: #> Actual #> Favored 0 0.5 1 #> TRUE 7 4 32 #> (tie) 0 0 0 #> FALSE 9 1 3 mc <- elo.markovchain(score(points.Home, points.Visitor) ~ team.Home + team.Visitor, data = tournament, subset = points.Home != points.Visitor, k = 0.7) summary(mc) #> #> An object of class 'elo.markovchain', containing information on 8 teams and 51 matches. #> #> Mean Square Error: 0.1688 #> AUC: 0.8 #> Favored Teams vs. Actual Wins: #> Actual #> Favored 0 1 #> TRUE 10 29 #> (tie) 0 0 #> FALSE 6 6 co <- elo.colley(score(points.Home, points.Visitor) ~ team.Home + team.Visitor, data = tournament, subset = points.Home != points.Visitor) summary(co) #> #> An object of class 'elo.colley', containing information on 8 teams and 51 matches. #> #> Mean Square Error: 0.1565 #> AUC: 0.8339 #> Favored Teams vs. Actual Wins: #> Actual #> Favored 0 1 #> TRUE 7 32 #> (tie) 0 0 #> FALSE 9 3 wp <- elo.winpct(score(points.Home, points.Visitor) ~ team.Home + team.Visitor, data = tournament, subset = points.Home != points.Visitor, k = 0.7) summary(wp) #> #> An object of class 'elo.winpct', containing information on 8 teams and 51 matches. #> #> Mean Square Error: 0.1566 #> AUC: 0.8339 #> Favored Teams vs. Actual Wins: #> Actual #> Favored 0 1 #> TRUE 7 32 #> (tie) 0 0 #> FALSE 9 3