playdata3 <- data.table::fread("../input/playtrack/tracklist2.csv", stringsAsFactors = F) final_data <- data.table::fread("../input/playtrack/final_data.csv", stringsAsFactors = F) injury <- data.table::fread("../input/nfl-playing-surface-analytics/InjuryRecord.csv", stringsAsFactors = F) library(dplyr) library(ggplot2) #velocity: calculated for each play using x-y coordinates #acceleration : calculated for each play using x-y coordinates #v_dirr : velocity direction angle for each play #acc_dirr: acceleration direction angle for each play #vdir_ch : velocity direction change for each play #accdir_ch: acceleration direction change for each play #aver_dir : direction change of player's motion for each play #aver_orient : direction change of player's orientation for each play #time : time duration for each play #dis: distance for each play stadfield<-ggplot(data = playdata3) + geom_bar(mapping = aes(x = StadiumType, fill = FieldType)) stadfield+coord_flip()+ ggtitle("OUTDOOR STADIUM IS THE MAJOR TYPE WITH NATURAL SURFACE") ggplot(playdata3, aes(Weather, velocity)) + geom_boxplot(fill = "red")+ scale_y_continuous("velocity", breaks= seq(0,10, by=1))+ labs(title = "EFFECT OF WEATHER ON VELOCITY", x = "Weather") #there is little differnce in velocity across different condition stad_vel<-ggplot(playdata3, aes(StadiumType, velocity)) + geom_boxplot(fill = "red")+ scale_y_continuous("velocity", breaks= seq(0,10, by=1))+ labs(title = "EFFECT OF STADIUM TYPE ON VELOCITY", x = "Stadium Type") stad_vel+coord_flip() #there is little differnce in velocity in different stadium types ggplot(playdata3, aes(FieldType, velocity)) + geom_boxplot(fill = "red")+ scale_y_continuous("velocity", breaks= seq(0,10, by=1))+ labs(title = "EFFECT OF FIELD TYPE ON VELOCITY", x = "Field Type") positionplot<-ggplot(playdata3, aes(Position, velocity)) + geom_boxplot(fill = "red")+ scale_y_continuous("velocity", breaks= seq(0,10, by=1))+ labs(title = "EFFECT OF Position TYPE ON VELOCITY", x = "Position Type") positionplot+coord_flip() plot5<-ggplot(playdata3, aes(RosterPosition,velocity)) + geom_boxplot(fill = "red")+ labs(title = "EFFECT OF ROSTER POSITION ON VELOCITY", x = "Roster Position") plot5+coord_flip() plot7<-ggplot(playdata3, aes(PlayType,velocity)) + geom_boxplot(fill = "red")+ labs(title = "EFFECT OF PLAY TYPE ON VELOCITY", x = "Play type") plot7+coord_flip() pgplot<-ggplot(playdata3, aes(PositionGroup, velocity)) + geom_boxplot(fill = "red")+ scale_y_continuous("velocity", breaks= seq(0,10, by=1))+ labs(title = "EFFECT OF Position Group Type ON VELOCITY", x = "Position Group Type") pgplot+coord_flip() ggplot(playdata3, aes(velocity)) + geom_histogram(binwidth = 2)+ scale_x_continuous("velocity", breaks = seq(0,10,by = 1))+ labs(title = "Velocity Histogram") ggplot(data =playdata3) + geom_smooth(mapping = aes(x = time, y =velocity))+ ggtitle("VELOCITY DECREASES WITH TIME") ggplot(data =playdata3) + geom_smooth(mapping = aes(x = dis, y =velocity)) ggplot(data =playdata3) + geom_smooth(mapping = aes(x = acceleration, y =velocity))+ ggtitle("Positive Linear Relationship between Acceleration and Velocity") ggplot(data = playdata3) + geom_smooth(mapping = aes(x = velocity, y = aver_orient, colour = FieldType))+ labs(title = "CHANGE IN ORIENTATION VS VELOCITY", x = "velocity", y = "change in orientation") ggplot(data = playdata3) + geom_smooth(mapping = aes(x = velocity, y = aver_dir, colour = FieldType))+ labs(title = "CHANGE IN MOTION DIRECTION VS VELOCITY", x = "velocity", y = "change in motion direction") ggplot(data = playdata3) + geom_smooth(mapping = aes(x = acceleration, y = aver_dir, colour = FieldType))+ labs(title = "CHANGE IN MOTION DIRECTION VS ACCELERATION", x = "Acceleration", y = "change in motion direction") ggplot(data = playdata3) + geom_smooth(mapping = aes(x = acceleration, y = aver_orient, colour = FieldType))+ labs(title = "CHANGE IN ORIENTATION VS ACCELERATION", x = "Acceleration", y = "change in orientation") plot3<-ggplot(playdata3, aes(StadiumType, aver_dir)) + geom_boxplot(fill = "blue")+ labs(title = "Effect Of Stadium Type ON Change in player's Motion Direction", x = "Stadium Type", y = "Change in motion direction" ) plot3+coord_flip() ploto<-ggplot(playdata3, aes(StadiumType, aver_orient)) + geom_boxplot(fill = "blue")+ labs(title = "Effect Of Stadium Type ON Change in player's orientation Direction", x = "Stadium Type", y = "Change in orientation" ) ploto+coord_flip() plotfd<-ggplot(playdata3, aes(FieldType, aver_orient)) + geom_boxplot(fill = "blue")+ labs(title = "Effect Of Field Type ON Change in player's motion Direction", x = "Field Type", y = "Change in motion" ) plotfd+coord_flip() plotfo<-ggplot(playdata3, aes(FieldType, aver_orient)) + geom_boxplot(fill = "blue")+ labs(title = "Effect Of Field Type ON Change in player's orientation Direction", x = "Field Type", y = "Change in orientation" ) plotfo+coord_flip() ggplot(playdata3, aes(Weather, acceleration)) + geom_boxplot(fill = "red")+ labs(title = "EFFECT OF Weather ON acceleration", x = "Weather") ggplot(playdata3, aes(FieldType, acceleration)) + geom_boxplot(fill = "red")+ labs(title = "EFFECT OF Field Type ON acceleration", x = "Field Type") ggplot(playdata3, aes(StadiumType, acceleration)) + geom_boxplot(fill = "red")+ labs(title = "EFFECT OF Stadium Type ON acceleration", x = "Stadium Type") #Velocity and acceleration models depend on stepwise model selection** Model_velocity<- lm(velocity~time+dis+aver_orient+vdir_ch+accdir_ch+acceleration+as.factor(FieldType)+Temperature+ StadiumType+Weather+RosterPosition+Position+PositionGroup+PlayType+PlayerGamePlay, data = playdata3) Model_acceleration<-lm(acceleration~aver_dir+vdir_ch+accdir_ch+velocity+dis+time+StadiumType+PlayType+RosterPosition+Position+PlayerDay+PlayerGamePlay, data = playdata3) summary(Model_velocity) summary(Model_acceleration) #**velocity model** #-our model estimates an expected .02217 decrease in velocity for every 1 second increase in time holding other variables constant #-our model estimates an expected 0.01625 increase in velocity for every 1 yard increase in distance holding other variables constant #our model estimates an expected .004325 decrease in velocity for every 1 degree increase in change in orientation angle holding other variables constant #our model estimates an expected .00331 increase in velocity for every 1 degree increase in change in velocity direction holding other variables constant #our model estimates an expected .006577 increase in velocity for every 1 degree increase in change in acceleration direction holding other variables constant #our model estimates an expected 3.012 increase in velocity for every 1 unit increase in acceleration holding other variables constant #our model estimates an expected 0.01269 inccrease in velocity for being asynthetic field holding other variables constant #our model estimates an expected 0.00001474 decrease in velocity for every 1 degree F increase in Temperature holding other variables constant #our model estimates an expected 0.01293 decrease in velocity for being indoor closed stadium holding other variables constant #our model estimates an expected 0.03725 increase in velocity for being indoor open stadium holding other variables constant #our model estimates an expected 0.03179 decrease in velocity in cloudy and rain weather holding other variables constant #our model estimates an expected 0.02035 increase in velocity for RosterPosition Defensive Lineman holding other variables constant #our model estimates an expected 0.07337 increase in velocity for RosterPosition kicker holding other variables constant #our model estimates an expected 0.2514 increase in velocity for RosterPosition Line backer holding other variables constant #our model estimates an expected 0.1035 increase in velocity for RosterPosition Offensive Lineman holding other variables constant #our model estimates an expected 0.04858 increase in velocity for RosterPosition safety holding other variables constant #our model estimates an expected 0.05933 increase in velocity for RosterPosition Tight end holding other variables constant #our model estimates an expected 0.08028 decrease in velocity for Position FS holding other variables constant #our model estimates an expected 0.04685 increase in velocity for Position ILB holding other variables constant #our model estimates an expected 0.07998 decrease in velocity for Position K holding other variables constant #our model estimates an expected 0.1089 increase in velocity for Position LB holding other variables constant #our model estimates an expected 0.03583 increase in velocity for Position MLB holding other variables constant #our model estimates an expected 0.08696 increase in velocity for Position S holding other variables constant #our model estimates an expected 0.01322 decrease in velocity for Position T holding other variables constant #our model estimates an expected 0.1672 decrease in velocity for Position Group DL holding other variables constant #our model estimates an expected 0.2179 decrease in velocity for Position Group LB holding other variables constant #our model estimates an expected 0.056 increase in velocity for play type extra point holding other variables constant #our model estimates an expected 0.07134 increase in velocity for play type field goal holding other variables constant #our model estimates an expected 0.2384 increase in velocity for play type kick off holding other variables constant #our model estimates an expected 0.21 increase in velocity for play type kick off not returned holding other variables constant #our model estimates an expected 0.1894 increase in velocity for play type kick off returned holding other variables constant #our model estimates an expected 0.1994 increase in velocity for play type punt holding other variables constant #our model estimates an expected 0.1934 increase in velocity for play type punt not returned holding other variables constant #our model estimates an expected 0.1812 increase in velocity for play type punt returned holding other variables constant #our model estimates an expected 0.000492 decrease in velocity for every 1 play increase in plays the player has participated in during the game holding other variables constant #38.61% from the variation in velocity explained by deviation in our independent variables. #**Acceleration model** # -our model estimates an expected .0007677 decrease in acceleration for every 1 degree increase in change in direction angle of player motion holding other variables constant #our model estimates an expected .003436 increase in acceleration for every 1 degree increase in change in velocity direction holding other variables constant #our model estimates an expected .0008413 decrease in acceleration for every 1 degree increase in change in acceleration direction holding other variables constant #our model estimates an expected 0.04705 increase in acceleration for every 1 unit increase in velocity holding other variables constant #our model estimates an expected .00153 decrease in acceleration for every 1 second increase in time holding other variables constant #our model estimates an expected 0.001822 decrease in acceleration for being indoor closed stadium holding other variables constant #our model estimates an expected 0.002085 decrease in acceleration for being indoor open stadium holding other variables constant #our model estimates an expected 0.001502 decrease in acceleration for being outdoor stadium holding other variables constant #our model estimates an expected 0.003062 decrease in acceleration for RosterPosition Line backer holding other variables constant #our model estimates an expected 0.0116 decrease in acceleration for RosterPosition Offensive Lineman holding other variables constant #our model estimates an expected 0.004073 increase in acceleration for RosterPosition wide reciever holding other variables constant #our model estimates an expected 0.005167 increase in acceleration for Position DB holding other variables constant #our model estimates an expected 0.006817 decrease in acceleration for Position DT holding other variables constant #our model estimates an expected 0.002644 increase in acceleration for Position FS holding other variables constant #our model estimates an expected 0.01185 increase in acceleration for Position LB holding other variables constant #our model estimates an expected 0.006758 decrease in acceleration for Position NT holding other variables constant #our model estimates an expected 0.007198 increase in acceleration for Position S holding other variables constant #our model estimates an expected 0.000003219 decrease in acceleration for every 1 day increase in timeline of player's participation in games holding other variables constant #our model estimates an expected 0.00001695 decrease in acceleration for every 1 play increase in plays the player has participated in during the game holding other variables constant #34.48% from the variation in acceleration explained by the deviation in our independent variables. #**Injury Assesment** #merged the 3 files in "final_data" summary(final_data) summary(injury) plotbb<-ggplot(data = injury) + geom_bar(mapping = aes(x = BodyPart, fill = BodyPart)) plotbb+coord_flip()+ ggtitle("Knee and Ankle Injuries are most common in injury ") plotbf<-ggplot(data = injury) + geom_bar(mapping = aes(x = Surface, fill = BodyPart)) plotbf+coord_flip()+ ggtitle("Injuries are more likely to be on Synthetic Surfaces") injury <- injury %>% mutate(severity = ifelse(DM_M42 == 1, "42", ifelse(DM_M42 == 0 & DM_M28 == 1, "28", ifelse(DM_M42 == 0 & DM_M28 == 0 & DM_M7 == 1, "7", "1")))) plotsev<-ggplot(data = injury) + geom_bar(mapping = aes(x = severity, fill = BodyPart)) plotsev+coord_flip()+ ggtitle("Most injuries with [7-28] interval") plotdm1<-ggplot(data = injury) + geom_bar(mapping = aes(x = severity, fill = BodyPart), position = "dodge")+ scale_x_discrete("severity", breaks = seq(0,1)) plotdm1+facet_grid(~severity) #Ankle injury is most common injury for missed days less than 7 #Knee injury is most common injury for missed days more than 7 and less than 28 #Knee injury is most common injury for missed days more than 28 and less than 42 #Knee injury is most common injury for missed days more than 42 plotsev1<-ggplot(data = injury) + geom_bar(mapping = aes(x = Surface, fill = severity)) plotsev1+coord_flip()+ ggtitle("Synthetic surface has more sever injuries") plotbs<-ggplot(data = final_data) + geom_bar(mapping = aes(x = StadiumType, fill = BodyPart)) plotbs+coord_flip()+ ggtitle("Injury is most common in outdoor Stadium") plota<-ggplot(data = final_data) + geom_bar(mapping = aes(x = StadiumType, fill = BodyPart))+ facet_grid(~FieldType) plota+coord_flip()+ ggtitle("Injury is most common in outdoor stadium with natural surface") plotc<-ggplot(data = final_data) + geom_bar(mapping = aes(x = Weather, fill =BodyPart)) plotc+coord_flip()+ ggtitle("Injury is most common in Cloudy weather") plotd<-ggplot(final_data, aes(BodyPart,velocity)) + geom_boxplot(fill = "blue")+ labs(title = "Effect of VELOCITY on Body Part injuries ", x = "Body Part") plotd+coord_flip() plote<-ggplot(final_data, aes(BodyPart,aver_dir)) + geom_boxplot(fill = "blue")+ labs(title = "Effect of Direction change in player's motion on Body Part injuries", x = "Body Part", y =" Direction change in player's motion") plote+coord_flip() ``` ```{r,echo=T} final_data<- final_data %>% mutate(severity = ifelse(DM_M42 == 1, "42", ifelse(DM_M42 == 0 & DM_M28 == 1, "28", ifelse(DM_M42 == 0 & DM_M28 == 0 & DM_M7 == 1, "7", "1")))) ``` ```{r,echo=T} plotda<-ggplot(final_data, aes(BodyPart,acceleration)) + geom_boxplot(fill = "blue")+ labs(title = "Effect of Acceleration on Body Part injuries ", x = "Body Part") plotda+coord_flip() ``` ```{r,echo=T} ploteor<-ggplot(final_data, aes(BodyPart,aver_orient)) + geom_boxplot(fill = "blue")+ labs(title = "Effect of Direction change in orientation on Body Part injuries", x = "Body Part", y =" Direction change in orientation") ploteor+coord_flip() ``` ```{r,echo=T} plotds<-ggplot(final_data, aes(severity,velocity)) + geom_boxplot(fill = "blue")+ labs(title = "Effect of VELOCITY on severity ", x = "Severity") plotds+coord_flip() ``` ```{r,echo=T} plotes<-ggplot(final_data, aes(severity,aver_dir)) + geom_boxplot(fill = "blue")+ labs(title = "Effect of Direction change in player motion on Severity", x = "Severity", y = " Direction change in player's motion") plotes+coord_flip() ``` ```{r,echo=T} plotsa<-ggplot(final_data, aes(severity,acceleration)) + geom_boxplot(fill = "blue")+ labs(title = "Effect of Acceleration on severity ", x = "severity") plotsa+coord_flip() ``` plotso<-ggplot(final_data, aes(BodyPart,aver_orient)) + geom_boxplot(fill = "blue")+ labs(title = "Effect of Direction change in orientation on severity", x = "severity", y =" Direction change in orientation") plotso+coord_flip() ``` plotpt<-ggplot(data = final_data) + geom_bar(mapping = aes(x = PlayType, fill = BodyPart)) plotpt+coord_flip()+ ggtitle("Pass play is major type related with injuries") plotpts<-ggplot(data = final_data) + geom_bar(mapping = aes(x = PlayType, fill = severity)) plotpts+coord_flip()+ ggtitle("Pass play has most sever injuries") plotpos<-ggplot(data = final_data) + geom_bar(mapping = aes(x = Position, fill = severity)) plotpos+coord_flip()+ ggtitle("Effect of Position on severity") plotposg<-ggplot(data = final_data) + geom_bar(mapping = aes(x = PositionGroup, fill = severity)) plotposg+coord_flip()+ ggtitle("Effect of Position Group on severity") plotrpos<-ggplot(data = final_data) + geom_bar(mapping = aes(x = RosterPosition, fill = severity)) plotrpos+coord_flip()+ ggtitle("Effect of Roster Position on severity") plotpos1<-ggplot(data = final_data) + geom_bar(mapping = aes(x = Position, fill = BodyPart)) plotpos1+coord_flip()+ ggtitle("Effect of Position on Body part injuries") plotposg1<-ggplot(data = final_data) + geom_bar(mapping = aes(x = PositionGroup, fill = BodyPart)) plotposg1+coord_flip()+ ggtitle("Effect of Position Group on Body part injuries") plotrpos1<-ggplot(data = final_data) + geom_bar(mapping = aes(x = RosterPosition, fill = BodyPart)) plotrpos1+coord_flip()+ ggtitle("Effect of Roster Position on Body part injuries") ggplot(final_data, aes(velocity)) + geom_histogram(binwidth = 2, fill = "blue")+ labs(title = "Velocity vs BODY parts Histogram")+ facet_grid(~BodyPart) ggplot(data = injury) + geom_bar(mapping = aes(x = severity, fill = severity))+ facet_grid(~BodyPart) #Ankle injury is most common with missed [1-7] days #Foot injury is most common with missed [28-42] days #Heel injury is common only with missed [7-28] days #Knee injury is most common with missed [7-28] days #Toes injury is most common with missed [7-28] days library(finalfit) playinj1<-as.data.frame(final_data)%>%summary_factorlist("StadiumType","BodyPart",p = F, add_dependent_label = T, cont_cut = 1) playinj1 playinj2<-as.data.frame(final_data)%>%summary_factorlist("BodyPart","Weather",p = F, add_dependent_label = T, cont_cut = 1) playinj2 playinj3<-as.data.frame(final_data)%>%summary_factorlist("BodyPart","PlayType",p = F, add_dependent_label = T, cont_cut = 1) playinj3 playinj4<-as.data.frame(injury)%>%summary_factorlist("severity","BodyPart",p = F, add_dependent_label = T, cont_cut = 1) playinj4 playinj5<-as.data.frame(final_data)%>%summary_factorlist("BodyPart","Position",p = F, add_dependent_label = T, cont_cut = 1) playinj5 playinj6<-as.data.frame(final_data)%>%summary_factorlist("BodyPart","PositionGroup",p = F, add_dependent_label = T, cont_cut = 1) playinj6 playinj7<-as.data.frame(final_data)%>%summary_factorlist("BodyPart","RosterPosition",p = F, add_dependent_label = T, cont_cut = 1) playinj7 results<-data.frame(sapply(final_data[,c(16:19,21:26)],tapply, final_data$BodyPart, function(x) {c(mean= mean(x), sd = sd(x))})) results final_data$severity<-as.numeric(final_data$severity) results1<-data.frame(sapply(final_data[,c(16:19,21:26)],tapply, final_data$severity, function(x) {c(mean= mean(x), sd = sd(x))})) results1 library(dplyr) injury$DM_M28[injury$DM_M42==1]<-0 injury$DM_M7[injury$DM_M42==1]<-0 injury$DM_M7[injury$DM_M28==1]<-0 injury$DM_M1[injury$DM_M42==1]<-0 injury$DM_M1[injury$DM_M28==1]<-0 injury$DM_M1[injury$DM_M7==1]<-0 model<-lm(injury$severity~injury$BodyPart+injury$Surface,data = injury) summary(model) #The foot injury is shown to be significant on the severity of the injury , As foot injury will be associated with increasing the length of missed days for foot-injured players. model1<-glm(injury$DM_M1~injury$BodyPart+injury$Surface,family = "binomial") summary(model1) model2<-glm(injury$DM_M7~injury$BodyPart+injury$Surface,family = "binomial") summary(model2) model3<-glm(injury$DM_M28~injury$BodyPart+injury$Surface,family = "binomial") summary(model3) model4<-glm(injury$DM_M42~injury$BodyPart+injury$Surface,family = "binomial") summary(model4) #The above models were carried out to specify which length of missed days due to injury would be more significant in case of foot injury. #It can be significantly proven that the injuries in foot are associated with a length of missed days for not less than 28 days and may be lasted for more than 42 days which can be interpretted in the form of probabilities as following: #probability of being foot-injured for more than 28 days and less than 42 days : (exp( 2.6146 )/(1+exp( 2.6146 )))*100 = 93.18 % #probability of being foot-injured for 42 days or more : (exp( 2.06723)/(1+exp(2.06723 )))*100 = 88.77 % final_data$DM_M28[final_data$DM_M42==1]<-0 final_data$DM_M7[final_data$DM_M42==1]<-0 final_data$DM_M7[final_data$DM_M28==1]<-0 final_data$DM_M1[final_data$DM_M42==1]<-0 final_data$DM_M1[final_data$DM_M28==1]<-0 final_data$DM_M1[final_data$DM_M7==1]<-0 library(dummies) final_data<-cbind.data.frame(final_data,dummy(final_data$BodyPart,sep = ",")) final_data<-cbind.data.frame(final_data,dummy(final_data$StadiumType,sep = ",")) final_data<-cbind.data.frame(final_data,dummy(final_data$Surface,sep = ",")) cor(final_data$`final_data,Ankle`,final_data$velocity) cor(final_data$`final_data,Foot`,final_data$velocity) cor(final_data$`final_data,Knee`,final_data$velocity) cor(final_data$`final_data,Foot`,final_data$acceleration) cor(final_data$`final_data,Knee`,final_data$acceleration) cor(final_data$`final_data,Ankle`,final_data$acceleration) cor(final_data$`final_data,Foot`,final_data$vdir_ch) cor(final_data$`final_data,Knee`,final_data$vdir_ch) cor(final_data$`final_data,Ankle`,final_data$vdir_ch) cor(final_data$`final_data,Foot`,final_data$accdir_ch) cor(final_data$`final_data,Knee`,final_data$accdir_ch) cor(final_data$`final_data,Ankle`,final_data$accdir_ch) cor(final_data$`final_data,Foot`,final_data$aver_dir) cor(final_data$`final_data,Knee`,final_data$aver_dir) cor(final_data$`final_data,Ankle`,final_data$aver_dir) cor(final_data$`final_data,Foot`,final_data$aver_orient) cor(final_data$`final_data,Knee`,final_data$aver_orient) cor(final_data$`final_data,Ankle`,final_data$aver_orient) cor(final_data$`final_data,Ankle`,final_data$`final_data,Natural`) cor(final_data$`final_data,Ankle`,final_data$`final_data,Synthetic`) cor(final_data$`final_data,Knee`,final_data$`final_data,Natural`) cor(final_data$`final_data,Knee`,final_data$`final_data,Synthetic`) cor(final_data$`final_data,Foot`,final_data$`final_data,Natural`) cor(final_data$`final_data,Foot`,final_data$`final_data,Synthetic`) cor(final_data$`final_data,Ankle`,final_data$DM_M1) cor(final_data$`final_data,Ankle`,final_data$DM_M7) cor(final_data$`final_data,Ankle`,final_data$DM_M28) cor(final_data$`final_data,Ankle`,final_data$DM_M42) cor(final_data$`final_data,Knee`,final_data$DM_M1) cor(final_data$`final_data,Knee`,final_data$DM_M7) cor(final_data$`final_data,Knee`,final_data$DM_M28) cor(final_data$`final_data,Knee`,final_data$DM_M42) cor(final_data$`final_data,Foot`,final_data$DM_M1) cor(final_data$`final_data,Foot`,final_data$DM_M7) cor(final_data$`final_data,Foot`,final_data$DM_M28) cor(final_data$`final_data,Foot`,final_data$DM_M42) cor(final_data$`final_data,Foot`,final_data$severity) cor(final_data$`final_data,Ankle`,final_data$severity) cor(final_data$`final_data,Foot`,final_data$severity) cor(final_data$`final_data,Foot`,final_data$severity) cor(final_data$`final_data,Ankle`,final_data$severity) cor(final_data$`final_data,Knee`,final_data$severity) cor(final_data$severity,final_data$`final_data,dome_closed`) cor(final_data$severity,final_data$`final_data,indoor closed`) cor(final_data$severity,final_data$`final_data,indoor open`) cor(final_data$severity,final_data$`final_data,outdoor`) cor(final_data$`final_data,Foot`,final_data$`final_data,dome_closed`) cor(final_data$`final_data,Ankle`,final_data$`final_data,dome_closed`) cor(final_data$`final_data,Knee`,final_data$`final_data,dome_closed`) cor(final_data$`final_data,Foot`,final_data$`final_data,indoor closed`) cor(final_data$`final_data,Ankle`,final_data$`final_data,indoor closed`) cor(final_data$`final_data,Knee`,final_data$`final_data,indoor closed`) cor(final_data$`final_data,Foot`,final_data$`final_data,indoor open`) cor(final_data$`final_data,Ankle`,final_data$`final_data,indoor open`) cor(final_data$`final_data,Knee`,final_data$`final_data,indoor open`) cor(final_data$`final_data,Foot`,final_data$`final_data,outdoor`) cor(final_data$`final_data,Ankle`,final_data$`final_data,outdoor`) cor(final_data$`final_data,Knee`,final_data$`final_data,outdoor`) cor(final_data$`final_data,Knee`,final_data$Temperature) cor(final_data$`final_data,Ankle`,final_data$Temperature) cor(final_data$`final_data,Foot`,final_data$Temperature) library(corrgram) corrgram(final_data[,c(6:29,34)], order=NULL, panel=panel.shade, text.panel=panel.txt, main="Correlogram") library(olsrr) model<-lm(final_data$severity~final_data$RosterPosition+final_data$PlayerDay+final_data$Position+final_data$PositionGroup+final_data$StadiumType+final_data$Temperature+final_data$Weather+final_data$PlayType+final_data$PlayerGamePlay+final_data$time+final_data$velocity+final_data$acceleration+final_data$dis+final_data$aver_dir+final_data$aver_orient+final_data$vdir_ch+final_data$accdir_ch+final_data$v_dirr+final_data$acc_dirr+final_data$BodyPart+final_data$Surface,data = final_data) ols_step_both_p(model, details=TRUE) #Playing on a synthetic field type is associated with increasing the risk of severe long duration injuries. #Foot injury is associated with increasing the risk of severe long duration injuries. #Playing on a sunny weather is associated with increasing the length of missed days due to injuries occured at that weather.