This document discusses using NFL Combine results to predict success in the league. It begins with an introduction and overview of the author's data and approach. It then reviews several studies that have examined the relationship between combine results and draft status or NFL performance. Some key findings from these studies are that combine results can predict draft position for certain positions like running back and defensive back. The document then discusses indexes like MockDraftable and SPARQ that visualize athleticism. It notes problems with only using these indexes to determine player quality. The author develops their own index using SPARQ ratings, combine times and Approximate Value to reflect NFL success. Their initial models did not fit the data well. They tried defining power and speed metrics but that

1. Using NFL Combine Results
to Predict League Success
Michael Saumenig

2. Introduction
• My Data
• NFL Combine Background
• Literature Review
• McGee and Burkett
• Kuzmits and Adams
• Sierer, Battaglini, Mihalik, Shields, and Tomasini
• Turning these Models into Indexes
• Overview of Mockdraftable and SPARQ
• Problems with these Indexes
• My Index

3. My Data
• NFL Combine Data gathered by armchairanalysis.com
• Courtesy of Prof. Maxwell
• MockDraftable Combine Visualizations
• Pro-football-reference.com
• Approximate Value, etc.
• 3sigmaathlete.com
• P-SPARQ scores

4. NFL Combine Background
• 40-yard dash
• Bench Press (225 lb. reps)
• Vertical Jump
• Broad Jump
• 20-yard Shuttle
• 3 Cone Drill
• 60-yard Shuttle
• Positional Drills
• Team interviews
• Physical Exam
• Drug Testing
• Wonderlic

5. Literature Review

6. The National Football League Combine: A
Reliable Predictor of Draft Status?
• McGee and Burkett
• Evaluated 326 NCAA players attending 2000 NFL Combine
• Step-wise regression procedure to generate if the 9 combine events
affect draft order and to rank the relevance of each event to
positional groups
• Results? Great at predicting RB, DB, WR draft position… QB very good,
linemen, linebackers, not so much…
• Importance? Shows which combine events are relevant for the
different positional groups, useful In development of performance
index later

7. McGee and Burkett’s Data and Methodology
• 2000 NFL Combine Data
• 326 Observations
• Regression Equations derived for 7 positional groups
• 2 by 11 analysis (two groups: rounds 1-2 and rounds 6-7 and the 9
performance metrics + height/weight)
• Correlation Matrix to examine relationship between combine events
• 10, 20, 40 yard sprints highly correlative (0.95)
• Sprints highly correlative with broad jump (0.86), Vertical Jump (0.81), and 20-
yard shuttle (0.86)
• Broad Jump and Vertical Jump highly Correlative (0.83)

8. Regression

9. McGee and Burkett’s Results
• Running Backs, Wide Receivers,
and Defensive Backs had perfect
predictability using the derived
regression equations.
• Quarterbacks very good (0.84)
• Linebackers, Defensive Linemen,
not great

10. The NFL Combine: Does It Predict
Performance in the National Football League?
• Kuzmits and Adams
• QB,RB,WR Combine data from 1999-2004
• No evidence of a statistical relationship between combine
performance and NFL success, except RB and 40-Yard Dash
• Players potentially invalidating Combine with specific training
programs
• Wonderlic does not matter
• Skill vs. Ability
• Just as knowing English doesn’t make someone good at public speaking,
Running fast does not make someone a good WR

11. Kuzmits and Adams’ Data and Methodology
• Combine Data 1999-2004
• 3 Years Salary Received
• 3 Years Games Played
• Correlative Analysis shows no relationship between combine tests
and professional football performance, except RB sprint times

12. Kuzmits and Adams’ Results

13. The National Football League Combine: Performance
Differences Between Drafted and Non-Drafted Players
Entering the 2004 and 2005 Drafts
• Sierer, Battaglini, Mihalik, Shields, and Tomasini
• 2004 and 2005 NFL combines (n = 321)
• Players categorized into
• Skill (WR,CB,SS,FS,RB)
• Big Skill (FB, LB, TE, DE)
• Linemen (C, OG, OT, DT,)
• Drafted Skill Players performed better at 40 yard dash, vertical jump, 20
yard shuttle, and 3 cone drill
• Drafted Big Skill Players performed better at 40 yard dash and 3 Cone Drill
• Drafted linemen performed better at 40 yard dash, 3 Cone Drill, and Bench
Press.

14. Sierer, Battaglini, Mihalik, Shields, and
Tomasini’s Data and Methodology
• 2004 and 2005 NFL Combine Data
• T-Testing to look for statistically significant differences in means
between drafted and non-drafted players
• Drafted vs. Non-Drafted Players
• Separated into 3 Groups
• Skill
• Big Skill
• Line

15. Sierer, Battaglini, Mihalik, Shields, and
Tomasini’s Results

16. Takeaways from these Analyses
• Similar Events can potentially be grouped together or have interactive
effects (speed events, quickness events, power events)
• All Regressions need to be broken into positional groups
• QB
• WR
• RB/LB
• DB
• OL/DL

17. Introducing “Athleticism” Visualization and Indexes
MockDraftable SPARQ

18. MockDraftable
• Athleticism “Webs” or “Radars” based on combine event
performance percentiles
• Provides immediate feedback as to the type of athlete a given player
is
• Explosive?
• Fast?
• Agile?
• Unathletic?

19. Quiz Time!

20. Guess the Player

21. Differences in Web Shape
“All-Around Athlete” “One Dimensional”

22. Pros and Cons of Visual Models
Pros
• Tidy Visualization
• One-Dimensional Players vs. All
Around Athlete
• For example, speedy gadget WRs vs
WR1s
Cons
• Are these players actually good?

23. Introducing SPARQ
• Speed, Power, Agility, Reaction, Quickness
• Combine Events:
• 40 Yard Dash
• Kneeling Power Ball Toss
• Agility Shuttle
• Yo-Yo Intermittent Recovery Test
• Verical Jump
• Founded in 2004 to create an “SAT” for High School Athletes
• Business Consists of Personal Training Programs, Athletic Apparel, and
Footwear
• Sold to Nike in 2009

24. Reverse Engineering SPARQ Scores
• Project of FieldGulls.com writers
Davis Hsu and Danny Kelly
• Interested in Pete Carroll and GM
John Schneider’s involvement in its
development
• Hype surrounding SPARQ’s use in
the 2013 NFL Draft led to Nike’s
Removal of the Calculator from its
website
• Zach Whitman finds close
approximation, which he calls
pSPARQ

25. Pete Carroll
• Seahawks Coach mysteriously
involved with Nike development
of SPARQ
• Mentions drafting players who
“test well” leading up to the
2013 NFL Draft

26. Whitman’s Methodology for finding pSPARQ
• 5 Variables for each of the five SPARQ exercises
• Transform Combine Events to SPARQ Events
• Diminishing Returns to athletic performance
• Helps players who struggle in one event

27. List of 3 Sigma Athletes
• These current NFL players are at least 3 Standard Deviations above
the mean:
• JJ Watt
• Lane Johnson
• Byron Jones

28. JJ Watt
• Career Achievements:
• 4x Pro Bowl
• 4x First Team All-Pro
• 3x NFL Defensive Player of the
Year
• 2x NFL Sacks Leader

29. Lane Johnson
• Career Achievements:
• Super Bowl LII Champion
• 1x Pro Bowl
• 1x First Team All-Pro

30. Byron Jones
• Career Achievements:

31. Problems With SPARQ / “3 Sigma Athletes”
• Once Again… Are these players actually good?
• The influence of height and weight on SPARQ scores
• All “3 Sigma” Athletes are abnormally large for their positions
• For linemen, all it takes is to be abnormally fast, for example

32. Introducing Approximate Value
• Pro-Football Reference’s “Attempt to put a single number on the
seasonal value of a player at any position from any year”
• Approximate Value (AV) is a substitute for statements like: “How
many seasons did X start?” “How many times did X make the Pro
Bowl?”

33. My Turn: Formulating a Predictive Index
• Methodology:
• Use SPARQ Ratings, Combine Times, and AV to form an index that actually
reflects how good players are in the NFL
• 2014 NFL Draft Data
• Unfortunately, only data available as a spreadsheet
• Some players never play in the NFL… therefore, no AV
• Only way is to drop all players with no AV….

34. My Data Set
• 2014 Combine Data – Drafted Players
• 2014 NFL Draft rSPARQ Data
• 2015 Approximate Values
• 229 Observations
• Unfortunately, limited by available rSPARQ data…
• No QBs? No Kickers?
• Random Players missing? Quincy Enunwa?

35. First Steps…
• rSPARQ and draft position? Not
too helpful…

36. Correlation Matrix

37. Sorted Correlation Matrix for SPARQ
• DB – 40 Yard Dash (-0.4481) Vertical (0.5085) Broad (0.3748)
• DL – 40 Yard Dash (-0.6810) Vertical (0.4179) Broad (0.3170)
• LB – 40 Yard Dash (-0.4400) Vertical (0.6472) Broad (0.6037)
• OL – 40 Yard Dash (-0.7170) Vertical (0.1484) Broad (0.1062)
• RB – 40 Yard Dash (-0.6420) Vertical (0.3895) Broad (0.2206)
• TE – 40 Yard Dash (-0.4496) Vertical (-0.2803) Broad (-0.2781)
• WR – 40 Yard Dash (-0.3633) Vertical (0.7324) Broad (-0.1413)

38. Lower 40, Higher Vertical, and
Higher Broad = Better SPARQ

39. My SPARQ Derivation…
• SPARQ = -.486*height + .291*weight – 56.203*40 yard dash +
.239*bench + 1.024*vertical - .089*broad – 3.966*shuttle + .192*3
Cone Drill + 329.505
• Jadeveon Clowney:
• -.486*77 + .291*266 – 56.203*4.51 + .239*21 + 1.024*37.5 - .089*124 –
3.966*4.43 + .192*7.27 + 329.505
• 132.2
• rSPARQ = 131.6

40. rSPARQ vs mSPARQ

41. mSPARQ Continued

42. Time for Approximate Value!

43. Comparing SPARQs
rSPARQ and AV mSPARQ and AV
0
5
10
15
20
25
0 20 40 60 80 100 120 140 160
APPROXIMATEVALUE
MSPARQ
APPROXIMATE VALUE
0
5
10
15
20
25
0 20 40 60 80 100 120 140 160
APPROXIMATEVALUE
RSPARQ SCORE
APPROXIMATE VALUE

45. My Model
• Approximate Value = – (1.68*40 yard dash) + (.033*Bench) – (.05*Vertical) –
(.02*Broad) + (.35*cone) + 11.15
• Is this a good model? Not really... Why is 3 Cone Positive? Low r-squared…

46. One More Try…
• Defining “Power” and “Speed”
• Power = Vertical*Broad*Bench
• Speed = 40*Shuttle*3-Cone
• Solves the negativity problems…
• But doesn’t fit the data….