last hacked on Jul 22, 2017

This project focuses on using basic survival analysis techniques to determine factors influencing career length of NFL running backs, employing Kaplan-Meier esimates and Cox Proportional Hazards modeling procedures with the aid of RStudio and its [survival]( library. We extract data from using .csv files for career statistics and [Beautiful Soup]( for physical measurements and maintain our dataset using [pandas]( We present our results in a visually appealing and easily comprehensible manner through the use of [ggplot2](, in addition to other R libraries employed for analytical purposes.
Motivation ========== ## Runningbacks don't last very long in the NFL <img src=""> <img src=""> <img src=""> Methodology- Web Scraping ========================= Getting started with the .csv file ---------------------------------- 1. Start by going here (<>) and selecting RB in the drop-down menu for Position. We are deeply grateful to be able to scrape their data without much consequence. 2. Next to the "Drafted Players" heading, there is an option labeled "Share & more" which we will click, yielding an option to generate a .csv file that is suitable for Microsoft Excel. This is the compressed data of 300 NFL running backs. You can literally cut and paste this whole file into your text processor of choice. (We use TextWrangler) 3. To get more observations from previous generations of NFL players, we go to the bottom of the table on the website and click "Next Page" and then repeat step 2 with one caveat: when cutting and pasting the raw data file, omit the first line with all of the columns' names. 4. With Step 3 in mind, repeat Step 2, 4 more times. You should have 1500 observations total. 5. Save your file as a .csv file and read into Python. We save it as nflrb\_data.csv and that is the name we use in the Python part. Yay! We are ready to plug this baby into Python. # Getting Started in Python We are using Python to obtain further measures of interest for the RBs in our dataset. Namely, we wish to extract each player's height and weight in order to compute their Body Mass Index. To accomplish this, we need to write a function that accesses each player's Pro-Football-Reference page, then extracts the player's height and weight by parsing the page's .html code and finding a familiar expression. We also store the player's height and weight in integer form in two new columns in our dataset. ## Importing the Required Libraries ```python import pandas as pd import numpy as np from bs4 import BeautifulSoup import requests ``` ## Cleaning the Dataset ```python nfl = pd.read_csv("nflrb_data.csv") ``` ```python ## getting rid of the useless column that is never going to be used nfl.drop(["Rk","Unnamed: 23"], axis=1, inplace=True) ``` ```python nfl.columns ``` Index(['Year', 'Rnd', 'Pick', 'Unnamed: 4', 'Pos', 'DrAge', 'Tm', 'From', 'To', 'AP1', 'PB', 'St', 'CarAV', 'G', 'GS', 'Att', 'Yds', 'TD', 'Rec', 'Yds.1', 'TD.1', 'College/Univ'], dtype='object') ```python nfl.columns = ['Year', 'Rnd', 'Pick', 'Player', 'Pos', 'DrAge', 'Tm', 'From', 'To', 'AP1', 'PB', 'St', 'CarAV', 'G', 'GS', 'Att', 'Yds', 'TD', 'Rec', 'Yds.1', 'TD.1', 'College/Univ'] ``` ### Getting Rid of Some Missing Data ```python nfl.isnull().sum() ``` Year 0 Rnd 0 Pick 0 Player 0 Pos 0 DrAge 344 Tm 0 From 361 To 361 AP1 0 PB 0 St 0 CarAV 361 G 361 GS 361 Att 450 Yds 450 TD 450 Rec 488 Yds.1 488 TD.1 488 College/Univ 2 dtype: int64 ```python print("Number of Observations:", nfl.shape[0]) ``` Number of Observations: 1500 ```python ## getting rid of the observations where not enough info could be found nfl = nfl[nfl["From"].isnull() == False] print("Number of Observations:", nfl.shape[0]) ``` Number of Observations: 1139 ```python ## getting rid of the players that did not retire by 2016 nfl = nfl[nfl["To"]!=2016] print("Number of Observations:", nfl.shape[0]) ``` Number of Observations: 1037 ```python nfl.isnull().sum() ``` Year 0 Rnd 0 Pick 0 Player 0 Pos 0 DrAge 0 Tm 0 From 0 To 0 AP1 0 PB 0 St 0 CarAV 0 G 0 GS 0 Att 88 Yds 88 TD 88 Rec 127 Yds.1 127 TD.1 127 College/Univ 0 dtype: int64 ## Finding New Data When the original .csv file was extracted, it contained a column that had the player's name along with a snippet of the URL that was part of their ProFootballReference page. This function returns a list containing the player's name and respective ProFootballReference URL. We run this function and then append the output sequentially to our existing dataset. ```python baseurl = "" def split_player(row): split_list = row["Player"].split("\\") player_name = split_list[0] player_url_code = split_list[1] first_letter = player_url_code[0] full_url = baseurl + first_letter + "/" + player_url_code + ".htm" return [player_name, full_url] a = nfl.apply(split_player,axis=1) ``` ```python # converted the lists into numpy arrays and then added them into the dataframe nfl["Player"] = np.array([row[0] for row in a]) nfl["PFR_URL"] = np.array([row[1] for row in a]) ``` Since the original .csv file did not contain the players' height and weight, this function was created to take in a player's respective URL and parse the website to find their height and weight using BeautifulSoup4. If the info could not be found, it would be assigned a missing data value using an error exception. ```python def player_info(row): response = requests.get(row["PFR_URL"]) content = response.content parser = BeautifulSoup(content, 'html.parser') try: height = parser.find_all(itemprop="height")[0].text weight = parser.find_all(itemprop="weight")[0].text except IndexError: height=weight=None return height, weight a = nfl.apply(player_info, axis=1) print(a.head()) ``` 35 (5-11, 229lb) 37 (6-1, 225lb) 40 (5-9, 215lb) 43 (6-0, 245lb) 45 (5-10, 209lb) dtype: object ```python nfl["Height"] = np.array([row[0] for row in a]) nfl["Weight"] = np.array([row[1] for row in a]) ``` ```python ## deleting the observations where no height or weight could be parsed nfl = nfl[nfl["Height"].isnull() == False] nfl = nfl[nfl["Weight"].isnull() == False] ``` ```python ## converting the height from character to integer def convert_height(row): height = row["Height"].split("-") converted_height = 12*int(height[0]) + int(height[1]) return converted_height nfl["Height"] = nfl.apply(convert_height,axis=1) ``` ```python ## converting the weight from character to integer def convert_weight(row): weight = int(row["Weight"][:3]) return weight nfl["Weight"] = nfl.apply(convert_weight, axis=1) ``` ```python ## Output the new dataframe into a new .csv file nfl.to_csv("nfl.csv") ``` Methodology- Analyis ==================== Our analysis will employ the theory of Survival Analysis, which measures survival probability and instantaneous rate of hazard for an event of interest over a given time period. We are interested in the amount of games (our time variable) it takes for an NFL runningback's professional career to end (our event of interest). Thanks to our web scraping process, we now have a large, informative and (somewhat) tidy dataset. It is now time to read our finished product (nfl.csv) from our Python program into R. nfl <- read.csv("filepath/nfl.csv") To install necessary packages, use: install.packages("package") To access the installed package: library(package) Here are the packages we used for our analysis: survival ggplot2 KMSurv flexsurv survminer We also make use of the ggsurv funtion, documented here: <> Further Tidying --------------- As it turns out, our dataset is still a bit on the messy side. We have some measures associated to each player that cannot be of use in survival analysis. We also do not have a variable set to represent our event of interest, retirement. We need to make a few adjustments before we can start our analysis. Tidying up: A few averages, and other stats: nfl$YPC <- nfl$Yds / nfl$Att nfl$Years <- nfl$To - nfl$From nfl$PB.1 <- ifelse(nfl$PB >= 1, 1, 0) #binary predictor nfl$AP1.1 <- ifelse(nfl$AP1 >= 1, 1, 0) nfl$BMI <- (nfl$Weight / (nfl$Height * nfl$Height)) * 703 We are now ready to begin performing survival analysis on the career lengths of NFL running backs. A Brief Overview of Survival Analysis ------------------------------------- - Two primary variables of interest for building models: - Duration of time until event or censoring - Binary indicator of event for each observation - 0; censored (left the study or did not experience event during study) - 1; experienced the event - Let T = Failure time (in the context of our study, T = games played until retirement) - Let *t*<sub>*i*</sub> denote a given time - Then we can define our HAZARD FUNCTION as: *h*(*t*<sub>*i*</sub>)=Pr(*T* = *t*<sub>*i*</sub>|*T* ≥ *t*<sub>*i*</sub>) - Our SURVIVAL FUNCTION is thus defined as: *S*(*T*)=∏<sub>*t*<sub>*i*</sub> ≤ *T*</sub>(1 − *h*(*t*<sub>*i*</sub>)) Kaplan-Meier Estimates: ----------------------- We estimate the survival function using Kaplan-Meier Estimation, which computes $\\hat{S}(T)$ as a function of each *t*<sub>*i*</sub> ∈ \[0, 1, ...239\]. These are best used when considering the entire subject population's aggregate survival with no additional covariates, or with a few discrete valued covariate levels. We make use of the ggsurv function here to create aesthetically pleasing survival plots: <img src='' > <img src='' > <img src='' > <img src='' > <img src='' > Cox Models: ----------- The KM Estimator is limited because it only considers a single homogenous population at a time. We use the Cox Proportional Hazards model to measure the effects of particular covariates on career survival, or more specifically, the instantaneous rates of hazard. With this very handy tool, we are able to judge the level at which covariates such as BMI influence a player's career length. Some theory of note: <img src=""> - Baseline hazard rate as a function of time (if all *X*<sub>*i*</sub> = 0 for a given observation, then *h*<sub>0</sub>(*t*) is the actual hazard rate) <!-- --> cox <- coxph(Surv(G,Retired)~BMI+YPC+DrAge, data = nfl.ret) summary(cox) ## Call: ## coxph(formula = Surv(G, Retired) ~ BMI + YPC + DrAge, data = nfl.ret) ## ## n= 932, number of events= 932 ## (82 observations deleted due to missingness) ## ## coef exp(coef) se(coef) z Pr(>|z|) ## BMI -0.07703 0.92586 0.01439 -5.352 8.71e-08 *** ## YPC -0.20421 0.81529 0.02986 -6.838 8.02e-12 *** ## DrAge 0.17489 1.19111 0.04337 4.033 5.52e-05 *** ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ## ## exp(coef) exp(-coef) lower .95 upper .95 ## BMI 0.9259 1.0801 0.9001 0.9524 ## YPC 0.8153 1.2266 0.7689 0.8644 ## DrAge 1.1911 0.8396 1.0941 1.2968 ## ## Concordance= 0.591 (se = 0.011 ) ## Rsquare= 0.078 (max possible= 1 ) ## Likelihood ratio test= 75.55 on 3 df, p=2.22e-16 ## Wald test = 92.89 on 3 df, p=0 ## Score (logrank) test = 87.74 on 3 df, p=0 Using AIC/BIC as a criterion for our model we find that our three most significant covariates are BMI, Yards/Carry and Draft Age. Our model is given by: *h*(*t*,*X*)=*h*<sub>0</sub>(*t*)\**e**x**p*((−.0770 \* *B**M**I*)+(−.2042 \* *Y**P**C*)+(.1749 \* *D**r**a**f**t**Ag**e*)) The Proportional Hazards Assumption ----------------------------------- The key assumption for the Cox Model is that covariate effects on survival are independent of time. We can test this using Schoenfeld Residuals. We are looking for a mean of 0 for the entire time duration, which suggests that errors are evenly distributed over time. Further, there is a p-value associated to each covariate. The hypotheses yielding each probability measure are: *H*<sub>0</sub>: The covariate's effect is independent of time *H*<sub>1</sub>: The covariate's effect exhibits time-dependency A low p-value indicates that we should consider omitting the associated covariate. <img src='' > We are thrilled with these results. Our model very much aligns with the Proportional Hazards assumption. Examining Our Model's Fit ------------------------- Now that we have a legitimate model in our hands, we can visualize the effects of different covariate levels on career survival: <img src='' > <img src='' > <img src='' > Fun With Our Model: A Tale of 3 RBs ----------------------------------- We can use our model to estimate real-life career survival probability. Here is a Kaplan-Meier estimate for SD Chargers legend Ladainian Tomlinson made from our Cox model: <img src='' > The black vertical line denotes the actual number of games LT played in his NFL career. For Ezekiel Elliott, we can provide an estimate for the probability he is still in the league after a given amount of games. He has only played 1 season (16 games), and given his measurements it is no surprise that he has made it this far: <img src='' > Here is the estimated career survival probability for Bo Jackson, arguably the greatest pure athlete in American history: <img src='' > Bo Jackson only played in three seasons, although he moonlighted as a star MLB player as well. His career was tragically ended by a catastrophic hip injury; the allegation is that the injury was worsened by Bo's prior steroid abuse. It is important to note here that statistical models such as KM estimates and Cox PH models are mere approximations of reality, and cannot in any way reliably predict real-life as it unfolds, but can be very useful to elucidate interesting relationships between two or more phenomena. Bonus: All-Pro vs. Pro-Bowl --------------------------- We figured that the Pro-Bowl and All-Pro covariates were likely highly significant in explaining career lengths amongst NFL players. However, these did not meet the Proportional Hazards assumption. Still seeking to employ the power of these covariates, we seek to find which accolade is more associated to a lengthy professional career. It should be noted that the presence of both accolades is the best indicator of a long playing career. <img src='' > <img src='' > The second plot is incomplete. We find that All-Pro status seems to imply being a Pro-Bowler as well. Generally, an All-Pro will not go long without being selected to a Pro-Bowl. Fitting a Distribution to Our Estimate: --------------------------------------- We can fit a parametric distribution to our overall survival curve. Observe: ## Call: ## flexsurvreg(formula = Surv(G, Retired) ~ 1, data = nfl.ret, dist = "gengamma") ## ## Estimates: ## est L95% U95% se ## mu 4.3805 4.2774 4.4837 0.0526 ## sigma 0.7168 0.6541 0.7856 0.0335 ## Q 1.6940 1.4332 1.9548 0.1331 ## ## N = 1014, Events: 1014, Censored: 0 ## Total time at risk: 58734 ## Log-likelihood = -5101.664, df = 3 ## AIC = 10209.33 <img src='' > We are impressed by the goodness of this fit. Our parameters for the distribution were estimated to be: (mu = 4.3805, sigma = .7168, Q = 1.694) More on the Generalized Gamma distribution can be found here: <> Summary and Closing Thoughts --------------------- For this project, our goal was to examine the statistical effects of career statistics, accolades and physical measurements on the career lengths of runningbacks in the NFL. Upon obtaining the dataset via Python methods, we switched to R and implemented theoretical tools of survival analysis such as the Kaplan Meier estimator and the Cox Proportional Hazards model. We found that there are three highly significant and time-independent covariates which can tell us a great deal about an NFL running back's potential career length: the age at which a player was drafted, the player's BMI, and the player's Yards per Carry statistic. Although we are happy with our results, we were displeased with how most of our selected covariates violated the Proportional Hazards assumption or confounded our estimates. Future iterations of this project will include extensions of the Cox model to accommodate such covariates in violation of the PH assumption. Key Results -------------------------- 1. Our estimated career survival curve for NFL RBs fits a Generalized Gamma Distribution with parameters (mu = 4.3805, sigma = .7168, Q = 1.694). 2. BMI is highly significant in predicting career length, having the largest magnitude of the three predictors in our model. Players with a higher computed BMI will last longer in the league. Since BMI loses predictive power in terms of indicating obesity when considering extremely muscular and athletic individuals (such as NFL RBs) the measure becomes one of body density. Specifically, the stockier a RB's build is, the longer we can expect them to last in the league. 3. Draft Age is highly significant in predicting career length. On average, the earlier a player enters the NFL, the longer it will take for their athletic ability to decrease to the point of seeking retirement from the league. 4. Yards per carry is also very significant. As an average measure, it is a prime indicator of how *good* an NFL RB is in their career. Of course, players with higher YPC can be expected to have lasted longer in the league. 5. All-Pro and Pro-Bowl status are indicators of a player's quality of play through their career. Perhaps trivially, players with these accolades lasted far longer than those who never acheived them. We found that All-Pro was a significantly better predictor of career longevity than Pro-Bowl. Acknowledgments --------------- <> <> <>


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