Has the NFL Combine’s 40 yard dash gotten faster ?

Last week on one of my favourite podcasts, ESPN’s Football Today, Matt Williamson & Kevin Weidl discussed the standout prospects from the NFL Combine. A lot of the conversation was around how the 40 yard dash times have improved year on year due to better training technique and specific training for the combine activities.

I wanted to see for myself and found Combine results for all participants going back to 1999 at nflcombineresults.com including last week’s 2013 results. This data set has key data for all 4,283 participants during this period and is a gold mine for analysis. The data needed a bit of cleaning up to get it into a data frame but if you’d like a copy then leave a comment or message via twitter (@minimalrblog) – I haven’t spent the time to work out how to use github to share datasets.

I compared the 40 yard dash times of 1999 and 2013 and initally didn’t see real improvements as the 5 best times were:

Name College Position Draft Year 40 Yard Time
Rondel Menendez Eastern Kentucky WR 1999 4.24
Marquise Goodwin Texas WR 2013 4.27
Champ Bailey Georgia CB 1999 4.28
Jay Hinton Morgan State (MD) RB 1999 4.29
Karsten Bailey Auburn WR 1999 4.33

The Combine class of 1999 had 6 of the best 10 times. However looking at the quartiles and plotting the 2 distributions showed a real improvement over the 14 years – while the fastest runners didn’t get faster, the rest of the field did benefit from improved training and technique.

Draft Year Fastest Time 1st Quartile Median 3rd Quartile Slowest Time
1999 4.24 4.61 4.78 5.09 5.84
2013 4.27 4.55 4.71 4.99 5.65


The overlapping distribution was generated using the ggplot2 library.

CombineData19992013 <- data.frame(CombineData[CombineData$Year == 1999 | CombineData$Year == 2013,])
ggplot(CombineData19992013, aes(X40Yard., fill = Year)) + geom_density(alpha = 0.2)


Visualising 2012 NFL Quarterback performance with R heat maps

With only 24 hours remaining in the 2012 NFL season, this is a good time to review how the league's QBs performed during the regular season using performance data from KFFL and the heat mapping capabilities of R.

#scale data to mean=0, sd=1 and convert to matrix
QBscaled <- as.matrix(scale(QB2012))

#create heatmap and don't reorder columns
pheatmap(QBscaled, cluster_cols=F, legend=FALSE, fontsize_row=12, fontsize_col=12, border_color=NA)


Instead of using the R's default heatmap, I've used the pheatmap function from the pheatmap library.

The analysis includes KFFL's data on Passes per Game, Passes Completed per Game, Pass Completion Rate, Pass Yards per Attempt, Pass Touchdowns per Attempt, Pass Interceptions per Attempt, Runs per Game, Run Yards per Attempt, Run Touchdowns per Attempt, 2 Point Conversions per Game, Fumbles per Game, Sacks per Game.

#cluster rows
hc.rows <- hclust(dist(QBscaled))


This cluster dendrogram shows 4 broad performance clusters of QBs who started at least half the regular season (8 games) plus Colin Kaepernick (7 games). It's important to remember this analysis does not include any playoff games. Our assessment of playoff QBs is also easily biased by the results of these games – just because Joe Flacco makes SuperBowl XLVII does not mean he has consistently outperformed Tom Brady.

Cluster 1 – The top tier passers

#draw heatmap for first cluster
pheatmap(QBscaled[cutree(hc.rows,k=4)==1,], cluster_cols=F, legend=FALSE, fontsize_row=12, fontsize_col=12, border_color=NA)


Pass first QBs with good passing stats and who kept out of trouble (low interceptions, sacks & fumbles). Within the group – Brees, Peyton Manning, Brady and Ryan have the best results with Carson Palmer a surprise in this group.

Cluster 2 – Successful run & pass QBs

#draw heatmap for second cluster
pheatmap(QBscaled[cutree(hc.rows,k=4)==2,], cluster_cols=F, legend=FALSE, fontsize_row=12, fontsize_col=12, border_color=NA)


Strong outcomes in both the passing and running game including the 3 QBs who led in run attempts per game – Newton, RG III and Kaepernick. RG III & Kaepernick also had surprisingly few interceptions per game given their propensity to aggressively throw deep.

Cluster 3 – The Middle

#draw heatmap for third cluster
pheatmap(QBscaled[cutree(hc.rows,k=4)==3,], cluster_cols=F, legend=FALSE, fontsize_row=12, fontsize_col=12, border_color=NA)


Not great but not the worse either including Joe Flacco.

Cluster 4 – A year of fumbles, interceptions and sacks

#draw heatmap for fourth cluster
pheatmap(QBscaled[cutree(hc.rows,k=4)==4,], cluster_cols=F, legend=FALSE, fontsize_row=12, fontsize_col=12, border_color=NA)


As a NY Jets supporter this is painful.