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Predicting NFL draft pick based on combine stats
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| *.csv |
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| \documentclass[12pt]{article} | |
| \usepackage{multirow} | |
| \usepackage[table,xcdraw]{xcolor} | |
| \begin{document} | |
| \begin{table}[h] | |
| \begin{tabular}{|c|c|c|c|} | |
| \hline | |
| \multicolumn{2}{|c|}{} & \multicolumn{2}{c|}{\textbf{Actual}} \\ \cline{3-4} | |
| \multicolumn{2}{|c|}{\multirow{-2}{*}{\textbf{QUARTERBACKS}}} & Drafted & Undrafted \\ \hline | |
| & Drafted & 3 & 3 \\ \cline{2-4} | |
| \multirow{-2}{*}{\textbf{Predicted}} & Undrafted & 13 & 15 \\ \hline | |
| \textit{F1-Score} & {\color[HTML]{3531FF} 27\%} & \textit{Accuracy} & {\color[HTML]{3531FF} 53\%} \\ \hline | |
| \end{tabular} | |
| \vspace{5mm} | |
| \begin{tabular}{|c|c|c|c|} | |
| \hline | |
| \multicolumn{2}{|c|}{} & \multicolumn{2}{c|}{\textbf{Actual}} \\ \cline{3-4} | |
| \multicolumn{2}{|c|}{\multirow{-2}{*}{\textbf{RUNNING BACKS}}} & Drafted & Undrafted \\ \hline | |
| & Drafted & 13 & 12 \\ \cline{2-4} | |
| \multirow{-2}{*}{\textbf{Predicted}} & Undrafted & 15 & 26 \\ \hline | |
| \textit{F1-Score} & {\color[HTML]{3531FF} 49\%} & \textit{Accuracy} & {\color[HTML]{3531FF} 59\%} \\ \hline | |
| \end{tabular} | |
| \vspace{5mm} | |
| \begin{tabular}{|c|c|c|c|} | |
| \hline | |
| \multicolumn{2}{|c|}{} & \multicolumn{2}{c|}{\textbf{Actual}} \\ \cline{3-4} | |
| \multicolumn{2}{|c|}{\multirow{-2}{*}{\textbf{WIDE RECEIVERS}}} & Drafted & Undrafted \\ \hline | |
| & Drafted & 14 & 4 \\ \cline{2-4} | |
| \multirow{-2}{*}{\textbf{Predicted}} & Undrafted & 15 & 51 \\ \hline | |
| \textit{F1-Score} & {\color[HTML]{3531FF} 60\%} & \textit{Accuracy} & {\color[HTML]{3531FF} 77\%} \\ \hline | |
| \end{tabular} | |
| \vspace{5mm} | |
| \begin{tabular}{|c|c|c|c|} | |
| \hline | |
| \multicolumn{2}{|c|}{} & \multicolumn{2}{c|}{\textbf{Actual}} \\ \cline{3-4} | |
| \multicolumn{2}{|c|}{\multirow{-2}{*}{\textbf{QUARTERBACKS}}} & Drafted & Undrafted \\ \hline | |
| & Drafted & 5 & 7 \\ \cline{2-4} | |
| \multirow{-2}{*}{\textbf{Predicted}} & Undrafted & 11 & 11 \\ \hline | |
| \textit{F1-Score} & {\color[HTML]{3531FF} 36\%} & \textit{Accuracy} & {\color[HTML]{3531FF} 47\%} \\ \hline | |
| \end{tabular} | |
| \vspace{5mm} | |
| \begin{tabular}{|c|c|c|c|} | |
| \hline | |
| \multicolumn{2}{|c|}{} & \multicolumn{2}{c|}{\textbf{Actual}} \\ \cline{3-4} | |
| \multicolumn{2}{|c|}{\multirow{-2}{*}{\textbf{RUNNING BACKS}}} & Drafted & Undrafted \\ \hline | |
| & Drafted & 12 & 9 \\ \cline{2-4} | |
| \multirow{-2}{*}{\textbf{Predicted}} & Undrafted & 16 & 29 \\ \hline | |
| \textit{F1-Score} & {\color[HTML]{3531FF} 49\%} & \textit{Accuracy} & {\color[HTML]{3531FF} 62\%} \\ \hline | |
| \end{tabular} | |
| \vspace{5mm} | |
| \begin{tabular}{|c|c|c|c|} | |
| \hline | |
| \multicolumn{2}{|c|}{} & \multicolumn{2}{c|}{\textbf{Actual}} \\ \cline{3-4} | |
| \multicolumn{2}{|c|}{\multirow{-2}{*}{\textbf{WIDE RECEIVERS}}} & Drafted & Undrafted \\ \hline | |
| & Drafted & 14 & 11 \\ \cline{2-4} | |
| \multirow{-2}{*}{\textbf{Predicted}} & Undrafted & 15 & 44 \\ \hline | |
| \textit{F1-Score} & {\color[HTML]{3531FF} 52\%} & \textit{Accuracy} & {\color[HTML]{3531FF} 69\%} \\ \hline | |
| \end{tabular} | |
| \end{table} | |
| \end{document} |
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| library(rpart.plot) | |
| library(randomForest) | |
| library(MASS) | |
| library(class) | |
| library(rpart) | |
| library(ROCR) | |
| getwd() | |
| setwd("C:\\Users\\Ilan Man\\Desktop\\Personal\\513750b0909ba1b06202") | |
| original_data = read.csv('combine.csv') | |
| data_2013 <- original_data[original_data$Year%in%c(2013,2012), | |
| names(original_data)%in% c("Year","Position","FortyYD", | |
| "Bench","Broad","Vertical", | |
| "TwentySS","HeightInchesTotal","PickTotal")] | |
| data <- original_data[original_data$Year<2012,names(original_data)%in% c("Year","Position","FortyYD", | |
| "Bench","Broad","Vertical", | |
| "TwentySS","HeightInchesTotal","PickTotal")] | |
| original_data$Picked <- ifelse(original_data$PickTotal==0,0,1) | |
| data$Picked <- ifelse(data$PickTotal==0,0,1) | |
| logistic_model <- function(train, test){ | |
| #### LOGISTIC REGRESSION #### | |
| full.fit <- glm(Picked ~., data=train,family='binomial') | |
| backwards = step(full.fit,trace=0) | |
| log.prediction <- predict(backwards,newdata=test,type='response') | |
| return(list(log.prediction,backwards)) | |
| } | |
| tree_model <- function(train, test){ | |
| #### DECISION TREES #### | |
| tree <- rpart(Picked~.,data=train) | |
| tree.prediction <- predict(tree,test[,-8]) | |
| return(list(tree.prediction,tree)) | |
| } | |
| rf_model <- function(train, test){ | |
| #### RANDOM FOREST #### | |
| forest <- randomForest(factor(Picked)~.,data=train, | |
| importance=TRUE,keep.forest=TRUE, | |
| ytest=as.factor(test$Picked),xtest=test[,-7],norm.votes=TRUE) | |
| return(list(forest$test$votes[,2],forest)) | |
| } | |
| knn_model <- function(train,test){ | |
| #### K-NEAREST NEIGHBORS #### | |
| numeric.train <- model.matrix(Picked ~. , data=train) | |
| numeric.test <- model.matrix(Picked ~. , data=test) | |
| s.train <- scale(numeric.train[,-1]) | |
| s.test <- scale(numeric.test[,-1]) | |
| knn.fit <- knn(s.train,s.test,cl = train$Picked,k=5,prob=T) | |
| return(attr(knn.fit,'prob')) | |
| } | |
| create_data <- function(data, position){ | |
| '"" | |
| Return a list of training and testing sets, and actual class labels, based on the position the user selects | |
| Perform light data clean up by removing the Year of the draft and the actual draft number, if drafted | |
| ""' | |
| data = data[data$Position==position,-c(which(names(data) %in% c("Position","Year","PickTotal")))] | |
| ## Use 1/3 of the data for training and 2/3 for testing | |
| ind <- sample(rownames(data),floor(nrow(data)/3)) | |
| train <- data[rownames(data) %in% ind,] | |
| test <- data[!(rownames(data) %in% ind),] | |
| actual <- as.factor(test[,7]) | |
| return(list(train, test, actual)) | |
| } | |
| score <- function(predictm,actual,plotting, model){ | |
| '"" | |
| Returns 3 classification metrics: F1-measure, AUC (Area under the ROC curve) and Accuracy | |
| Optionally, plot the ROC curve and accuracy curve for different false positive and true positive rates | |
| ""' | |
| pred <- prediction(as.numeric(predictm),actual) | |
| fperf <- performance(pred,'f') | |
| fm <- max(fperf@y.values[[1]],na.rm=T) | |
| acperf <- performance(pred,'acc') | |
| acc <- max(acperf@y.values[[1]],na.rm=T) | |
| aucm <- performance(pred,'auc')@y.values[[1]] | |
| if(plotting==T){ | |
| plot(performance(pred,'tpr','fpr'),main=paste('model roc\n',model)) | |
| plot(acperf,main=paste('model accuracy\n',model)) | |
| } | |
| return(data.frame(aucm,fm,acc)) | |
| } | |
| get_ensemble_results <- function(data, position, plotting){ | |
| '"" | |
| Return a list consisting of: | |
| 1) data frame of results from an ensemble of models used to predict whether or not a player | |
| gets drafted based on their stats from the combine | |
| 2) the models | |
| ""' | |
| ## Pull out data specific to the position | |
| new_data <- create_data(data,position) | |
| train <- new_data[[1]] | |
| test <- new_data[[2]] | |
| actual <- new_data[[3]] | |
| tm <- tree_model(train, test) | |
| lm <- logistic_model(train,test) | |
| km <- knn_model(train, test) | |
| rm <- rf_model(train, test) | |
| ## Run scoring function on four models | |
| ## Plot ROC and Accuracy curve, optional | |
| tree_output <- score(tm[[1]],actual,plotting,'tree') | |
| logistic_output <- score(lm[[1]],actual,plotting,'logistic') | |
| knn_output <- score(km,actual,plotting,'knn') | |
| rf_output <- score(rm[[1]],actual,plotting,'randomForest') | |
| ## Create data frame of results | |
| models = c("Decision Tree","Logistic Regression","K-nearest neighbors","Random Forest") | |
| f1_scores = c(tree_output$fm,logistic_output$fm,knn_output$fm,rf_output$fm) | |
| auc_scores = c(tree_output$aucm,logistic_output$aucm,knn_output$aucm,rf_output$aucm) | |
| acc_scores = c(tree_output$acc,logistic_output$acc,knn_output$acc,rf_output$acc) | |
| ensemble_summary = data.frame(Model=models, | |
| F1=f1_scores, | |
| AUC=auc_scores, | |
| ACC=acc_scores) | |
| return(list(ensemble_summary,tm[[2]],lm[[2]],rm[[2]])) | |
| } | |
| final_results <- function(pos){ | |
| for (p in pos){ | |
| ensemble_summary = get_ensemble_results(data, position=p,plotting=F) | |
| all_models = ensemble_summary[[1]] | |
| print(p) | |
| t_m = ensemble_summary[[2]] | |
| l_m = ensemble_summary[[3]] | |
| r_m = ensemble_summary[[4]] | |
| d_2013 <- data_2013[data_2013$Position==p,-c(which(names(data_2013) %in% c("Position","Year")))] | |
| d_2013$Picked <- factor(ifelse(d_2013$PickTotal>0,1,0)) | |
| d_2013$PickTotal <- NULL | |
| actual_2013 <- ifelse(d_2013$Picked==0,0,1) | |
| rf_prediction <- predict(r_m,d_2013[,-7],'prob') | |
| lm_prediction <- predict(l_m,d_2013,type='response') | |
| tm_prediction <- predict(t_m,d_2013) | |
| results <- rbind(score(rf_prediction[,2],actual_2013,TRUE,'randomForest'), | |
| score(lm_prediction,actual_2013,TRUE,'logistic'), | |
| score(tm_prediction,actual_2013,TRUE,'tree')) | |
| summary_results <- cbind(model=c("Random Forest","Logistic Regression","Decision Trees"),results) | |
| print(summary_results) | |
| } | |
| } | |
| pos = c('QB','RB','WR','CB','OLB') | |
| final_results(pos) | |
| ## Compare models to hueristic | |
| h_pos = pos[3] | |
| ensemble_summary = get_ensemble_results(data, position=h_pos,plotting=F) | |
| all_models = ensemble_summary[[1]] | |
| t_m = ensemble_summary[[2]] | |
| l_m = ensemble_summary[[3]] | |
| r_m = ensemble_summary[[4]] | |
| d_2013 <- data_2013[data_2013$Position==h_pos,-c(which(names(data_2013) %in% c("Position","Year")))] | |
| d_2013$Picked <- factor(ifelse(d_2013$PickTotal>0,1,0)) | |
| d_2013$PickTotal <- NULL | |
| actual_2013 <- ifelse(d_2013$Picked==0,0,1) | |
| players <- original_data[original_data$Year%in%c(2013,2012), | |
| names(original_data)%in%c("Name","PickTotal","Position")] | |
| players$Picked <- ifelse(players$PickTotal==0,0,1) | |
| players$PickTotal <- NULL | |
| players <- players[players$Position==h_pos,] | |
| rf_prediction <- predict(r_m,d_2013[,-7],'prob') | |
| lm_prediction <- predict(l_m,d_2013,type='response') | |
| tm_prediction <- predict(t_m,d_2013) | |
| rf_class <- predict(r_m,d_2013[,-7],'class') | |
| l_class <- ifelse(lm_prediction>=0.5,1,0) | |
| t_class <- ifelse(tm_prediction>=0.5,1,0) | |
| table(rf_class,players$Picked) | |
| table(l_class,players$Picked) | |
| table(t_class,players$Picked) | |
| high_jumpers <- d_2013$Vertical>median(d_2013$Vertical) | |
| fast <- d_2013$FortyYD<median(d_2013$FortyYD) | |
| hueristic <- ifelse(high_jumpers|fast,1,0) | |
| table(hueristic,players$Picked) | |
| ## DATA FOR PLOTTING ROC CURVES IN D3 | |
| ## Select best model at each position for use in ROC curve | |
| roc_wr <- predict(l_m,actual_2013,type='response') | |
| pred_wr <- prediction(as.numeric(roc_wr),actual_2013) | |
| wr.x <- performance(pred_wr,'tpr','fpr')@x.values[[1]] | |
| wr.y <- performance(pred_wr,'tpr','fpr')@y.values[[1]] | |
| write.csv(cbind(wr.x,wr.y),"wr.csv") | |
| roc_olb <- predict(l_m,d_2013,type='response') | |
| pred_olb <- prediction(as.numeric(roc_olb),actual_2013) | |
| olb.x <- performance(pred_o,'tpr','fpr')@x.values[[1]] | |
| olb.y <- performance(pred_o,'tpr','fpr')@y.values[[1]] | |
| write.csv(cbind(olb.x,olb.y),"olb.csv") | |
| roc_rb <- rf_prediction[,2] | |
| pred_rb <- prediction(as.numeric(roc_rb),actual_2013) | |
| rb.x <- performance(pred_rb,'tpr','fpr')@x.values[[1]] | |
| rb.y <- performance(pred_rb,'tpr','fpr')@y.values[[1]] | |
| write.csv(cbind(rb.x,rb.y),"rb.csv") | |
| roc_qb <- predict(l_m,d_2013) | |
| pred_qb <- prediction(as.numeric(roc_qb),actual_2013) | |
| qb.x <- performance(pred_qb,'tpr','fpr')@x.values[[1]] | |
| qb.y <- performance(pred_qb,'tpr','fpr')@y.values[[1]] | |
| write.csv(cbind(qb.x,qb.y),"qb.csv") |
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| library(ggplot2) | |
| library(gridExtra) | |
| library(grid) | |
| getwd() | |
| setwd("C:\\Users\\Ilan Man\\Desktop\\Personal\\513750b0909ba1b06202") | |
| original_data = read.csv('combine.csv') | |
| levels(data$Position) | |
| WR = original_data[original_data$Position=='WR'&original_data$Year>2005,] | |
| QB = original_data[original_data$Position=='QB'&original_data$Year>2005,] | |
| RB = original_data[original_data$Position=='RB'&original_data$Year>2005,] | |
| CB = original_data[original_data$Position=='CB'&original_data$Year>2005,] | |
| ############ | |
| # FORTY YARD DASH | |
| ############ | |
| RB_f <- data.frame(cbind(RB$Year,RB$FortyYD)) | |
| names(RB_f) <- c("Year","FortyYD") | |
| RB_f <- RB_f[!RB_f$FortyYD==0,] | |
| rf <- ggplot(RB_f,aes(x=Year,y=FortyYD))+geom_boxplot(aes(group=RB_f$Year))+ | |
| ggtitle("Running Back\nFortyYD")+ylab("Seconds")+ | |
| scale_x_continuous(breaks = seq(min(RB_f$Year), max(RB_f$Year), by = 1)) | |
| QB_f <- data.frame(cbind(QB$Year,QB$FortyYD)) | |
| names(QB_f) <- c("Year","FortyYD") | |
| QB_f <- QB_f[!QB_f$FortyYD==0,] | |
| qf <- ggplot(QB_f,aes(x=Year,y=FortyYD))+geom_boxplot(aes(group=QB_f$Year))+ | |
| ggtitle("QB FortyYD")+ylab("Seconds")+ | |
| scale_x_continuous(breaks = seq(min(QB_f$Year), max(QB_f$Year), by = 1)) | |
| CB_f <- data.frame(cbind(CB$Year,CB$FortyYD)) | |
| names(CB_f) <- c("Year","FortyYD") | |
| CB_f <- CB_f[!CB_f$FortyYD==0,] | |
| cf <- ggplot(CB_f,aes(x=Year,y=FortyYD))+geom_boxplot(aes(group=CB_f$Year))+ | |
| ggtitle("CB FortyYD")+ylab("Seconds")+ | |
| scale_x_continuous(breaks = seq(min(CB_f$Year), max(CB_f$Year), by = 1)) | |
| WR_f <- data.frame(cbind(WR$Year,WR$FortyYD)) | |
| names(WR_f) <- c("Year","FortyYD") | |
| WR_f <- WR_f[!WR_f$FortyYD==0,] | |
| wf <- ggplot(WR_f,aes(x=Year,y=FortyYD))+geom_boxplot(aes(group=WR_f$Year))+ | |
| ggtitle("CB FortyYD")+ylab("Seconds")+ | |
| scale_x_continuous(breaks = seq(min(WR_f$Year), max(WR_f$Year), by = 1)) | |
| grid.arrange(rf,wf,cf,qf) | |
| ############ | |
| # VERTICAL | |
| ############ | |
| RB_v <- data.frame(cbind(RB$Year,RB$Vertical)) | |
| names(RB_v) <- c("Year","Vertical") | |
| RB_v <- RB_v[!RB_v$Vertical==0,] | |
| rv <- ggplot(RB_v,aes(x=Year,y=Vertical))+geom_boxplot(aes(group=RB_v$Year))+ | |
| ggtitle("RB Vertical")+ylab("Inches")+ylim(20,45)+ | |
| scale_x_continuous(breaks = seq(min(RB_v$Year), max(RB_v$Year), by = 1)) | |
| QB_v <- data.frame(cbind(QB$Year,QB$Vertical)) | |
| names(QB_v) <- c("Year","Vertical") | |
| QB_v <- QB_v[!QB_v$Vertical==0,] | |
| qv <- ggplot(QB_v,aes(x=Year,y=Vertical))+geom_boxplot(aes(group=QB_v$Year))+ | |
| ggtitle("QB Vertical")+ylab("Inches")+ylim(20,45)+ | |
| scale_x_continuous(breaks = seq(min(QB_v$Year), max(QB_v$Year), by = 1)) | |
| CB_v <- data.frame(cbind(CB$Year,CB$Vertical)) | |
| names(CB_v) <- c("Year","Vertical") | |
| CB_v <- CB_v[!CB_v$Vertical==0,] | |
| cv <- ggplot(CB_v,aes(x=Year,y=Vertical))+geom_boxplot(aes(group=CB_v$Year))+ | |
| ggtitle("CB Vertical")+ylab("Inches")+ylim(20,45)+ | |
| scale_x_continuous(breaks = seq(min(CB_v$Year), max(CB_v$Year), by = 1)) | |
| WR_v <- data.frame(cbind(WR$Year,WR$Vertical)) | |
| names(WR_v) <- c("Year","Vertical") | |
| WR_v <- WR_v[!WR_v$Vertical==0,] | |
| wv <- ggplot(WR_v,aes(x=Year,y=Vertical))+geom_boxplot(aes(group=WR_v$Year))+ | |
| ggtitle("WR Vertical")+ylab("Inches")+ylim(20,45)+ | |
| scale_x_continuous(breaks = seq(min(WR_v$Year), max(WR_v$Year), by = 1)) | |
| grid.arrange(qv,rv,cv,wv) | |
| ############ | |
| # Broad | |
| ############ | |
| RB_b <- data.frame(cbind(RB$Year,RB$Broad)) | |
| names(RB_b) <- c("Year","Broad") | |
| RB_b <- RB_b[!RB_b$Broad==0,] | |
| rb <- ggplot(RB_b,aes(x=Year,y=Broad))+geom_boxplot(aes(group=RB_b$Year))+ | |
| ggtitle("RB Broad")+ylab("Inches")+ylim(90,140)+ | |
| scale_x_continuous(breaks = seq(min(RB_b$Year), max(RB_b$Year), by = 1)) | |
| QB_b <- data.frame(cbind(QB$Year,QB$Broad)) | |
| names(QB_b) <- c("Year","Broad") | |
| QB_b <- QB_b[!QB_b$Broad==0,] | |
| qb <- ggplot(QB_b,aes(x=Year,y=Broad))+geom_boxplot(aes(group=QB_b$Year))+ | |
| ggtitle("QB Broad")+ylab("Inches")+ylim(90,140)+ | |
| scale_x_continuous(breaks = seq(min(QB_b$Year), max(QB_b$Year), by = 1)) | |
| CB_b <- data.frame(cbind(CB$Year,CB$Broad)) | |
| names(CB_b) <- c("Year","Broad") | |
| CB_b <- CB_b[!CB_b$Broad==0,] | |
| cb <- ggplot(CB_b,aes(x=Year,y=Broad))+geom_boxplot(aes(group=CB_b$Year))+ | |
| ggtitle("CB Broad")+ylab("Inches")+ylim(90,140)+ | |
| scale_x_continuous(breaks = seq(min(CB_b$Year), max(CB_b$Year), by = 1)) | |
| WR_b <- data.frame(cbind(WR$Year,WR$Broad)) | |
| names(WR_b) <- c("Year","Broad") | |
| WR_b <- WR_b[!WR_b$Broad==0,] | |
| wb <- ggplot(WR_b,aes(x=Year,y=Broad))+geom_boxplot(aes(group=WR_b$Year))+ | |
| ggtitle("WR Broad")+ylab("Inches")+ylim(90,140)+ | |
| scale_x_continuous(breaks = seq(min(WR_b$Year), max(WR_b$Year), by = 1)) | |
| grid.arrange(qb,rb,cb,wb) |
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| // Set the dimensions of the canvas / graph | |
| var margin = {top: 30, right: 20, bottom: 70, left: 50}, | |
| width = 400 - margin.left - margin.right, | |
| height = 350 - margin.top - margin.bottom; | |
| // Set the ranges | |
| var x = d3.scale.linear().range([0, width]); | |
| var y = d3.scale.linear().range([height, 0]); | |
| // Define the axes | |
| var xAxis = d3.svg.axis().scale(x) | |
| .orient("bottom").ticks(5); | |
| var yAxis = d3.svg.axis().scale(y) | |
| .orient("left").ticks(5); | |
| // Define the line | |
| var valueline = d3.svg.line() | |
| .interpolate("basis") | |
| .x(function(d) { return x(d.X); }) | |
| .y(function(d) { return y(d.QB); }); | |
| var valueline2 = d3.svg.line() | |
| .interpolate("basis") | |
| .x(function(d) { return x(d.X); }) | |
| .y(function(d) { return y(d.WR); }); | |
| var valueline3 = d3.svg.line() | |
| .interpolate("basis") | |
| .x(function(d) { return x(d.X); }) | |
| .y(function(d) { return y(d.OLB); }); | |
| var valueline4 = d3.svg.line() | |
| .interpolate("basis") | |
| .x(function(d) { return x(d.X); }) | |
| .y(function(d) { return y(d.RB); }); | |
| var valueline5 = d3.svg.line() | |
| .interpolate("basis") | |
| .x(function(d) { return x(d.X); }) | |
| .y(function(d) { return y(d.USELESS); }); | |
| var tip = d3.tip() | |
| .attr('class', 'd3-tip') | |
| .style("visibility","visible") | |
| .offset([-20, 0]) | |
| .html(function(d) { | |
| return "Quarterback"; | |
| }); | |
| var tip2 = d3.tip() | |
| .attr('class', 'd3-tip') | |
| .style("visibility","visible") | |
| .offset([-20, 0]) | |
| .html(function(d) { | |
| return "Wide Receiver"; | |
| }); | |
| var tip3 = d3.tip() | |
| .attr('class', 'd3-tip') | |
| .style("visibility","visible") | |
| .offset([-20, 0]) | |
| .html(function(d) { | |
| return "Offensive Line Backer"; | |
| }); | |
| var tip4 = d3.tip() | |
| .attr('class', 'd3-tip') | |
| .style("visibility","visible") | |
| .offset([-20, 0]) | |
| .html(function(d) { | |
| return "Running Back"; | |
| }); | |
| var tip5 = d3.tip() | |
| .attr('class', 'd3-tip') | |
| .style("visibility","visible") | |
| .offset([-20, 0]) | |
| .html(function(d) { | |
| return "Useless"; | |
| }); | |
| // Adds the svg canvas | |
| var svg = d3.select("body") | |
| .append("svg") | |
| .attr("width", width + margin.left + margin.right) | |
| .attr("height", height + margin.top + margin.bottom) | |
| .append("g") | |
| .attr("transform", | |
| "translate(" + margin.left + "," + margin.top + ")"); | |
| data = [{"X":"0","WR":"0.2","OLB":"0.36363636","QB":"0.11111111","RB":"0.13157895","USELESS":"0"}, | |
| {"X":"0.001","WR":"0.2","OLB":"0.36363636","QB":"0.11111111","RB":"0.13157895","USELESS":"0.001"}, | |
| {"X":"0.002","WR":"0.2","OLB":"0.36363636","QB":"0.11111111","RB":"0.13157895","USELESS":"0.002"}, | |
| {"X":"0.003","WR":"0.2","OLB":"0.36363636","QB":"0.11111111","RB":"0.13157895","USELESS":"0.003"}, | |
| {"X":"0.004","WR":"0.2","OLB":"0.36363636","QB":"0.11111111","RB":"0.13157895","USELESS":"0.004"}, | |
| {"X":"0.005","WR":"0.2","OLB":"0.36363636","QB":"0.11111111","RB":"0.13157895","USELESS":"0.005"}, | |
| {"X":"0.006","WR":"0.2","OLB":"0.36363636","QB":"0.11111111","RB":"0.13157895","USELESS":"0.006"}, | |
| {"X":"0.007","WR":"0.2","OLB":"0.36363636","QB":"0.11111111","RB":"0.13157895","USELESS":"0.007"}, | |
| {"X":"0.008","WR":"0.2","OLB":"0.36363636","QB":"0.11111111","RB":"0.13157895","USELESS":"0.008"}, | |
| {"X":"0.009","WR":"0.2","OLB":"0.36363636","QB":"0.11111111","RB":"0.13157895","USELESS":"0.009"}, | |
| {"X":"0.01","WR":"0.2","OLB":"0.36363636","QB":"0.11111111","RB":"0.13157895","USELESS":"0.01"}, | |
| {"X":"0.011","WR":"0.2","OLB":"0.36363636","QB":"0.11111111","RB":"0.13157895","USELESS":"0.011"}, | |
| {"X":"0.012","WR":"0.2","OLB":"0.36363636","QB":"0.11111111","RB":"0.13157895","USELESS":"0.012"}, | |
| {"X":"0.013","WR":"0.2","OLB":"0.36363636","QB":"0.11111111","RB":"0.13157895","USELESS":"0.013"}, | |
| {"X":"0.014","WR":"0.2","OLB":"0.36363636","QB":"0.11111111","RB":"0.13157895","USELESS":"0.014"}, | |
| {"X":"0.015","WR":"0.2","OLB":"0.36363636","QB":"0.11111111","RB":"0.13157895","USELESS":"0.015"}, | |
| {"X":"0.016","WR":"0.2","OLB":"0.36363636","QB":"0.11111111","RB":"0.13157895","USELESS":"0.016"}, | |
| {"X":"0.017","WR":"0.2","OLB":"0.36363636","QB":"0.11111111","RB":"0.13157895","USELESS":"0.017"}, | |
| {"X":"0.018","WR":"0.2","OLB":"0.36363636","QB":"0.11111111","RB":"0.13157895","USELESS":"0.018"}, | |
| {"X":"0.019","WR":"0.2","OLB":"0.36363636","QB":"0.11111111","RB":"0.13157895","USELESS":"0.019"}, | |
| {"X":"0.02","WR":"0.2","OLB":"0.36363636","QB":"0.11111111","RB":"0.13157895","USELESS":"0.02"}, | |
| {"X":"0.021","WR":"0.2","OLB":"0.36363636","QB":"0.11111111","RB":"0.13157895","USELESS":"0.021"}, | |
| {"X":"0.022","WR":"0.2","OLB":"0.36363636","QB":"0.11111111","RB":"0.13157895","USELESS":"0.022"}, | |
| {"X":"0.023","WR":"0.2","OLB":"0.36363636","QB":"0.11111111","RB":"0.13157895","USELESS":"0.023"}, | |
| {"X":"0.024","WR":"0.2","OLB":"0.36363636","QB":"0.11111111","RB":"0.13157895","USELESS":"0.024"}, | |
| {"X":"0.025","WR":"0.2","OLB":"0.36363636","QB":"0.11111111","RB":"0.13157895","USELESS":"0.025"}, | |
| {"X":"0.026","WR":"0.2","OLB":"0.36363636","QB":"0.11111111","RB":"0.13157895","USELESS":"0.026"}, | |
| {"X":"0.027","WR":"0.2","OLB":"0.36363636","QB":"0.11111111","RB":"0.13157895","USELESS":"0.027"}, | |
| {"X":"0.028","WR":"0.2","OLB":"0.36363636","QB":"0.11111111","RB":"0.13157895","USELESS":"0.028"}, | |
| {"X":"0.029","WR":"0.2","OLB":"0.36363636","QB":"0.11111111","RB":"0.13157895","USELESS":"0.029"}, | |
| {"X":"0.03","WR":"0.2","OLB":"0.36363636","QB":"0.11111111","RB":"0.13157895","USELESS":"0.03"}, | |
| {"X":"0.031","WR":"0.2","OLB":"0.36363636","QB":"0.11111111","RB":"0.13157895","USELESS":"0.031"}, | |
| {"X":"0.032","WR":"0.2","OLB":"0.36363636","QB":"0.11111111","RB":"0.13157895","USELESS":"0.032"}, | |
| {"X":"0.033","WR":"0.2","OLB":"0.36363636","QB":"0.11111111","RB":"0.13157895","USELESS":"0.033"}, | |
| {"X":"0.034","WR":"0.2","OLB":"0.36363636","QB":"0.11111111","RB":"0.13157895","USELESS":"0.034"}, | |
| {"X":"0.035","WR":"0.38181818","OLB":"0.36363636","QB":"0.11111111","RB":"0.13157895","USELESS":"0.035"}, | |
| {"X":"0.036","WR":"0.38181818","OLB":"0.36363636","QB":"0.11111111","RB":"0.15789474","USELESS":"0.036"}, | |
| {"X":"0.037","WR":"0.38181818","OLB":"0.36363636","QB":"0.11111111","RB":"0.15789474","USELESS":"0.037"}, | |
| {"X":"0.038","WR":"0.38181818","OLB":"0.36363636","QB":"0.11111111","RB":"0.15789474","USELESS":"0.038"}, | |
| {"X":"0.039","WR":"0.38181818","OLB":"0.36363636","QB":"0.11111111","RB":"0.15789474","USELESS":"0.039"}, | |
| {"X":"0.04","WR":"0.38181818","OLB":"0.36363636","QB":"0.11111111","RB":"0.15789474","USELESS":"0.04"}, | |
| {"X":"0.041","WR":"0.38181818","OLB":"0.36363636","QB":"0.11111111","RB":"0.15789474","USELESS":"0.041"}, | |
| {"X":"0.042","WR":"0.38181818","OLB":"0.36363636","QB":"0.11111111","RB":"0.15789474","USELESS":"0.042"}, | |
| {"X":"0.043","WR":"0.38181818","OLB":"0.36363636","QB":"0.11111111","RB":"0.15789474","USELESS":"0.043"}, | |
| {"X":"0.044","WR":"0.38181818","OLB":"0.36363636","QB":"0.11111111","RB":"0.15789474","USELESS":"0.044"}, | |
| {"X":"0.045","WR":"0.38181818","OLB":"0.36363636","QB":"0.11111111","RB":"0.15789474","USELESS":"0.045"}, | |
| {"X":"0.046","WR":"0.38181818","OLB":"0.36363636","QB":"0.11111111","RB":"0.15789474","USELESS":"0.046"}, | |
| {"X":"0.047","WR":"0.38181818","OLB":"0.36363636","QB":"0.11111111","RB":"0.15789474","USELESS":"0.047"}, | |
| {"X":"0.048","WR":"0.38181818","OLB":"0.36363636","QB":"0.11111111","RB":"0.15789474","USELESS":"0.048"}, | |
| {"X":"0.049","WR":"0.38181818","OLB":"0.36363636","QB":"0.11111111","RB":"0.15789474","USELESS":"0.049"}, | |
| {"X":"0.05","WR":"0.38181818","OLB":"0.36363636","QB":"0.11111111","RB":"0.15789474","USELESS":"0.05"}, | |
| {"X":"0.051","WR":"0.38181818","OLB":"0.36363636","QB":"0.11111111","RB":"0.15789474","USELESS":"0.051"}, | |
| {"X":"0.052","WR":"0.38181818","OLB":"0.36363636","QB":"0.11111111","RB":"0.15789474","USELESS":"0.052"}, | |
| {"X":"0.053","WR":"0.38181818","OLB":"0.36363636","QB":"0.11111111","RB":"0.15789474","USELESS":"0.053"}, | |
| {"X":"0.054","WR":"0.38181818","OLB":"0.36363636","QB":"0.11111111","RB":"0.15789474","USELESS":"0.054"}, | |
| {"X":"0.055","WR":"0.38181818","OLB":"0.36363636","QB":"0.11111111","RB":"0.15789474","USELESS":"0.055"}, | |
| {"X":"0.056","WR":"0.38181818","OLB":"0.36363636","QB":"0.11111111","RB":"0.15789474","USELESS":"0.056"}, | |
| {"X":"0.057","WR":"0.38181818","OLB":"0.36363636","QB":"0.11111111","RB":"0.15789474","USELESS":"0.057"}, | |
| {"X":"0.058","WR":"0.38181818","OLB":"0.36363636","QB":"0.11111111","RB":"0.15789474","USELESS":"0.058"}, | |
| {"X":"0.059","WR":"0.38181818","OLB":"0.36363636","QB":"0.11111111","RB":"0.15789474","USELESS":"0.059"}, | |
| {"X":"0.06","WR":"0.38181818","OLB":"0.36363636","QB":"0.11111111","RB":"0.15789474","USELESS":"0.06"}, | |
| {"X":"0.061","WR":"0.38181818","OLB":"0.36363636","QB":"0.11111111","RB":"0.15789474","USELESS":"0.061"}, | |
| {"X":"0.062","WR":"0.38181818","OLB":"0.36363636","QB":"0.11111111","RB":"0.15789474","USELESS":"0.062"}, | |
| {"X":"0.063","WR":"0.38181818","OLB":"0.36363636","QB":"0.22222222","RB":"0.15789474","USELESS":"0.063"}, | |
| {"X":"0.064","WR":"0.38181818","OLB":"0.36363636","QB":"0.22222222","RB":"0.15789474","USELESS":"0.064"}, | |
| {"X":"0.065","WR":"0.38181818","OLB":"0.36363636","QB":"0.22222222","RB":"0.15789474","USELESS":"0.065"}, | |
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| {"X":"0.073","WR":"0.4","OLB":"0.36363636","QB":"0.22222222","RB":"0.23684211","USELESS":"0.073"}, | |
| {"X":"0.074","WR":"0.4","OLB":"0.36363636","QB":"0.22222222","RB":"0.23684211","USELESS":"0.074"}, | |
| {"X":"0.075","WR":"0.4","OLB":"0.36363636","QB":"0.22222222","RB":"0.23684211","USELESS":"0.075"}, | |
| {"X":"0.076","WR":"0.4","OLB":"0.36363636","QB":"0.22222222","RB":"0.23684211","USELESS":"0.076"}, | |
| {"X":"0.077","WR":"0.4","OLB":"0.36363636","QB":"0.22222222","RB":"0.23684211","USELESS":"0.077"}, | |
| {"X":"0.078","WR":"0.4","OLB":"0.36363636","QB":"0.22222222","RB":"0.23684211","USELESS":"0.078"}, | |
| {"X":"0.079","WR":"0.4","OLB":"0.36363636","QB":"0.22222222","RB":"0.23684211","USELESS":"0.079"}, | |
| {"X":"0.08","WR":"0.4","OLB":"0.36363636","QB":"0.22222222","RB":"0.23684211","USELESS":"0.08"}, | |
| {"X":"0.081","WR":"0.4","OLB":"0.36363636","QB":"0.22222222","RB":"0.23684211","USELESS":"0.081"}, | |
| {"X":"0.082","WR":"0.4","OLB":"0.36363636","QB":"0.22222222","RB":"0.23684211","USELESS":"0.082"}, | |
| {"X":"0.083","WR":"0.4","OLB":"0.36363636","QB":"0.22222222","RB":"0.23684211","USELESS":"0.083"}, | |
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| {"X":"0.864","WR":"0.94545455","OLB":"1","QB":"1","RB":"0.97368421","USELESS":"0.864"}, | |
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| {"X":"0.866","WR":"0.94545455","OLB":"1","QB":"1","RB":"0.97368421","USELESS":"0.866"}, | |
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| {"X":"0.873","WR":"0.94545455","OLB":"1","QB":"1","RB":"0.97368421","USELESS":"0.873"}, | |
| {"X":"0.874","WR":"0.94545455","OLB":"1","QB":"1","RB":"0.97368421","USELESS":"0.874"}, | |
| {"X":"0.875","WR":"0.94545455","OLB":"1","QB":"1","RB":"0.97368421","USELESS":"0.875"}, | |
| {"X":"0.876","WR":"0.94545455","OLB":"1","QB":"1","RB":"0.97368421","USELESS":"0.876"}, | |
| {"X":"0.877","WR":"0.94545455","OLB":"1","QB":"1","RB":"0.97368421","USELESS":"0.877"}, | |
| {"X":"0.878","WR":"0.94545455","OLB":"1","QB":"1","RB":"0.97368421","USELESS":"0.878"}, | |
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| {"X":"0.894","WR":"0.94545455","OLB":"1","QB":"1","RB":"0.97368421","USELESS":"0.894"}, | |
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| {"X":"0.896","WR":"0.94545455","OLB":"1","QB":"1","RB":"0.97368421","USELESS":"0.896"}, | |
| {"X":"0.897","WR":"0.96363636","OLB":"1","QB":"1","RB":"0.97368421","USELESS":"0.897"}, | |
| {"X":"0.898","WR":"0.96363636","OLB":"1","QB":"1","RB":"0.97368421","USELESS":"0.898"}, | |
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| {"X":"0.902","WR":"0.96363636","OLB":"1","QB":"1","RB":"0.97368421","USELESS":"0.902"}, | |
| {"X":"0.903","WR":"0.96363636","OLB":"1","QB":"1","RB":"0.97368421","USELESS":"0.903"}, | |
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| {"X":"0.949","WR":"1","OLB":"1","QB":"1","RB":"1","USELESS":"0.949"}, | |
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| {"X":"0.967","WR":"1","OLB":"1","QB":"1","RB":"1","USELESS":"0.967"}, | |
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| {"X":"0.969","WR":"1","OLB":"1","QB":"1","RB":"1","USELESS":"0.969"}, | |
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| {"X":"0.975","WR":"1","OLB":"1","QB":"1","RB":"1","USELESS":"0.975"}, | |
| {"X":"0.976","WR":"1","OLB":"1","QB":"1","RB":"1","USELESS":"0.976"}, | |
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| {"X":"0.978","WR":"1","OLB":"1","QB":"1","RB":"1","USELESS":"0.978"}, | |
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| {"X":"0.988","WR":"1","OLB":"1","QB":"1","RB":"1","USELESS":"0.988"}, | |
| {"X":"0.989","WR":"1","OLB":"1","QB":"1","RB":"1","USELESS":"0.989"}, | |
| {"X":"0.99","WR":"1","OLB":"1","QB":"1","RB":"1","USELESS":"0.99"}, | |
| {"X":"0.991","WR":"1","OLB":"1","QB":"1","RB":"1","USELESS":"0.991"}, | |
| {"X":"0.992","WR":"1","OLB":"1","QB":"1","RB":"1","USELESS":"0.992"}, | |
| {"X":"0.993","WR":"1","OLB":"1","QB":"1","RB":"1","USELESS":"0.993"}, | |
| {"X":"0.994","WR":"1","OLB":"1","QB":"1","RB":"1","USELESS":"0.994"}, | |
| {"X":"0.995","WR":"1","OLB":"1","QB":"1","RB":"1","USELESS":"0.995"}, | |
| {"X":"0.996","WR":"1","OLB":"1","QB":"1","RB":"1","USELESS":"0.996"}, | |
| {"X":"0.997","WR":"1","OLB":"1","QB":"1","RB":"1","USELESS":"0.997"}, | |
| {"X":"0.998","WR":"1","OLB":"1","QB":"1","RB":"1","USELESS":"0.998"}, | |
| {"X":"0.999","WR":"1","OLB":"1","QB":"1","RB":"1","USELESS":"0.999"}, | |
| {"X":"1","WR":"1","OLB":"1","QB":"1","RB":"1","USELESS":"1"}] | |
| // Scale the range of the data | |
| x.domain([0, 1]); | |
| y.domain([0, 1]); | |
| svg.call(tip); | |
| svg.call(tip2); | |
| svg.call(tip3); | |
| svg.call(tip4); | |
| svg.call(tip5); | |
| // Add the valueline path. | |
| svg.append("path") | |
| .attr("class", "line") | |
| .attr("d", valueline(data)) | |
| .on('mouseover', tip.show) | |
| .on("mousemove", function(){return tip.style("top", (event.pageY-10)+"px"). | |
| style("left",(event.pageX+10)+"px");}) | |
| .on('mouseout', tip.hide); | |
| svg.append("path") | |
| .style("stroke", "red") | |
| .attr("d", valueline2(data)) | |
| .on('mouseover', tip2.show) | |
| .on("mousemove", function(){return tip2.style("top", (event.pageY-10)+"px"). | |
| style("left",(event.pageX+10)+"px");}) | |
| .on('mouseout', tip2.hide); | |
| svg.append("path") | |
| .style("stroke", "green") | |
| .attr("d", valueline3(data)) | |
| .on('mouseover', tip3.show) | |
| .on("mousemove", function(){return tip3.style("top", (event.pageY-10)+"px"). | |
| style("left",(event.pageX+10)+"px");}) | |
| .on('mouseout', tip3.hide); | |
| svg.append("path") | |
| .style("stroke", "purple") | |
| .attr("d", valueline4(data)) | |
| .on('mouseover', tip4.show) | |
| .on("mousemove", function(){return tip4.style("top", (event.pageY-10)+"px"). | |
| style("left",(event.pageX+10)+"px");}) | |
| .on('mouseout', tip4.hide); | |
| svg.append("path") | |
| .style("stroke", "black") | |
| .attr("d", valueline5(data)) | |
| .on('mouseover', tip5.show) | |
| .on("mousemove", function(){return tip5.style("top", (event.pageY-10)+"px"). | |
| style("left",(event.pageX+10)+"px");}) | |
| .on('mouseout', tip5.hide); | |
| // Add the X Axis | |
| svg.append("g") | |
| .attr("class", "x axis") | |
| .attr("transform", "translate(0," + height + ")") | |
| .call(xAxis) | |
| .append("text") | |
| .attr("x", width / 2) | |
| .attr("y", 40 ) | |
| .style("text-anchor", "middle") | |
| .text("1 - False Negative Rate"); | |
| // Add the Y Axis | |
| svg.append("g") | |
| .attr("class", "y axis") | |
| .call(yAxis) | |
| .append("text") | |
| .attr("transform", "rotate(-90)") | |
| .attr("y", 0 - margin.left + 10 ) | |
| .attr("x", 0 - height/1.6 ) | |
| .style("font-size","12px") | |
| .style("text-anchor", "left") | |
| .text("True Positive Rate"); | |
| svg.append("text") | |
| .attr("x", (width / 2)) | |
| .attr("y", 0 - (margin.top / 2)) | |
| .attr("text-anchor", "middle") | |
| .style("font-size", "16px") | |
| .text("ROC Curve for by Position"); | |
| //}); | |
| </script> | |
| </body> |
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