Welcome to Project Left Turn

Welcome to Project Left Turn, a fun look at using statistics to understand the Indiana University Little 500. I completed this project in 2011 as an MBA student at the Kelley School of Business in Bloomington, IN.

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2011 Race Predictions

It probably comes as no surprise that the model is predicting the Cutters to be this year’s winner. The rest of the field is as follows:

Place

Team

1

Cutters

2

Delta Tau Delta

3

Sigma Chi

4

Phi Delta Theta

5

Beta Theta Pi

6

Black Key Bulls

7

Phi Gamma Delta

8

Sigma Nu

9

Phi Kappa Psi

10

Delta Chi

11

Acacia

12

Cru Cycling

13

Gray Goat

14

Hoosier Climber?

15

Kappa Sigma

16

Theta Chi

17

Air Force Cycling

18

Emanon

19

Delta Upsilon

20

#JungleExpress

21

Pi Kappa Alpha

22

Achtung

23

Sigma Phi Epsilon

24

Sigma Alpha Mu

25

Evans Scholars

26

Dodds House

27

LAMP

28

Sigma Pi

29

Wright Cycling

30

Phi Kappa Sigma

31

CSF Cycling

32

Delta Sigma Pi

33

Sigma Alpha Epsilon

 

Five teams are within the standard error of the model and thus have a shot at the win. Winning probabilities for the top five teams are listed below.

Team

Chance  of Winning
Cutters

35%

Delta Tau Delta

15%
Sigma Chi

13%

Phi Delta Theta

12%
Beta Theta Pi

8%

How it works:

A regression of historical data reveals that 68% of the race outcome can be explained by ITT times, team pursuit times, and qualification times. The ITT time of the team’s fastest rider and the team pursuit time were the most significant variables to explain race outcome. The ITT time of the third rider was insignificant and not included in the regression model. The model can predict a team’s race time to within 161 seconds. The expected race times are assumed to be normally distributed. This assumptions allows for simulation of 1000 iterations using the standard error of 161. This revealed each team’s chances of winning as listed above.

The model assumes that a team’s four fastest riders ride in the race. This assumption is, of course, imperfect but it provides the only simple way to regress ITT times to race outcome. All times are normalized to their respective yearly averages. This accounts for anomalies in weather, track conditions, and excessive yellow flags.

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Can you beat the computer?

We all know the Little 500 is mostly unpredictable. And even I know that a purely stastitical approach may not be the best way to make predictions. It is for this reason I would like to know: can you beat the computer?

If you think you can, email me your predictions by noon on Wednesday. Send me an excel spreadsheet with a list of men’s teams and finishing positions. Email files to eandreol@indiana.edu with subject “Prediction Contest”.  Predictions must be made for the entire field of 33 teams. After the race I will compare your predictions to my computer predictions by caclulating the correlation coeficient.

As an added bonus the submitter with the highest correlation coefficient will recieve a free Little 5 themed Kilroy’s poster courtesy of Kilroy’s Was Here. Winners will be announced after the race.

Best of luck. Look for my race predictions Wednesday at noon!

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Why we run the race

Saturday’s team pursuit results prove that all Little 500 event outcomes can be unpredictable. My excel model, which was based on ITTs and quals, had Cutters winning by 12 seconds. Instead, Cutters finished third and Delts secured the win, despite the computer only giving Delts a 9.3% chance of winning.

Here is a look at each team’s actual versus predicted results.

Team

Actual

Predicted

Delta Tau Delta

1 4

Sigma Chi

2 7
Cutters 3

1

Phi Delta Theta 4

2

Beta Theta Pi

5 3
Black Key Bulls 6

5

Phi Gamma Delta

7 9
Hoosier Climber? 8

12

Gray Goat Cycling

9 16
Cru Cycling 10

11

Delta Chi

11 10
#JungleExpress 12

27

Phi Kappa Psi

13 8
Kappa Sigma 14

13

Wright Cycling

15 29
Sigma Nu 16

6

Theta Chi

17 14
Delta Upsilon 18

18

Sigma Pi

19  
Emanon 20

21

CSF Cycling

21  
Acacia 22

17

Sigma Phi Epsilon

23 22
Achtung 24

28

Cru Cycling B

25  
Air Force Cycling 26

15

Dodds House

27 19
Sigma Alpha Mu 28

20

Phi Kappa Sigma

29 26
LAMP 30

23

Sigma Alpha Epsilon

31 31
Delta Sigma Pi 32

25

 

The predictions were not as accurate as last year’s race predictions, but still performed better than expected with a .78 correlation coefficient. Twenty teams were predicted within 5 spots of their actual performance.

Look for race predictions to be posted by Wednesday.

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Team Pursuit Predictions

With team pursuit underway, I wanted to make some predictions. It should be noted that the prediction model assumes that all qualifying teams (except CSF Cycling and Sigma Pi) will be riding in TP. This is already not accurate, as it seems like a few teams have not shown up.

The predictions are based on multivariate regression of historic qualification and ITT times. Significant variables were found to be a team’s fastest rider, a team’s third fastest rider, and a team’s average adjusted qualification time. These variables combine to explain approximately 57% of TP results. The model is not as strong as the race-day model, and should not be taken too seriously.

Here are the predicted results:

1 Cutters
2 Phi Delta Theta
3 Beta Theta Pi
4 Delta Tau Delta
5 Black Key Bulls
6 Sigma Nu
7 Sigma Chi
8 Phi Kappa Psi
9 Phi Gamma Delta
10 Delta Chi
11 Cru Cycling
12 Hoosier Climber?
13 Kappa Sigma
14 Theta Chi
15 Air Force Cycling
16 Gray Goat
17 Acacia
18 Delta Upsilon
19 Dodds House
20 Sigma Alpha Mu
21 Emanon
22 Sigma Phi Epsilon
23 LAMP
24 Pi Kappa Alpha
25 Delta Sigma Pi
26 Phi Kappa Sigma
27 #JungleExpress
28 Achtung
29 Wright Cycling
30 Evans Scholars
31 Sigma Alpha Epsilon

 

The model predicts the Cutters will beat Phi Delt by as much as 12 seconds, with the next four teams all being within 5 seconds of each other.

I will follow up tomorrow to compare the actual results.

 

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How much does team pursuit matter?

Earlier this week, I posted a riveting analysis on the usefulness of quals as a predictor of race outcome. Scroll down or click here to read to the full scoop.

With team pursuit coming up in a few days, I think it is worthwhile to perform the same analysis with TP results.

Using the same statistical techniques, it can be concluded that a team’s TP position can explain 68% of a team’s race day outcome. Thus TP performs much better than quals as a sole predictor of race day performance.

While a team only has an 8.5% chance of finishing the race in its exact TP position, it is important to note that 70% of teams finish within 5 spots of their TP position. 96% of teams finish within 10 spots and no team has ever finished the race more than 16 spots away from its TP position.

Here is a look at TP winners and their following race performance. (TP was cancelled in 2006 and 2002)

 

Year TP Winner Race Finish
2010 Phi Delta Theta 2
2009 Black Key Bulls 5
2008 Cutters 1
2007 Cutters 1
2005 Phi Gamma Delta 2
2004 Cutters 1
2003 Gafombi 1
2001 Cutters 7
2000 Sigma Phi Epsilon 8

 

 

For the nerds: Here is the scatterplot. Notice how there is less scatter as compared to the quals plot.

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A note on ITTs

After looking through men’s ITT data, I was certainly impressed by the 4 second difference between first and second place. In fact, Young receives the honor of enjoying the largest margin of victory in this decade.

A more intriguing question, however, is how Young’s time compares to the overall field. By analyzing each year’s top 50 average, we can determine how the winning time compares to the 50 fastest riders.

Year Winning Rider Winning Time Top 50 Average Difference
2011 Eric Young 142.00 151.94 9.94
2010 Eric Young 142.09 150.41 8.32
2009 Eric Young 138.25 147.20 8.95
2008 Issac Neff 139.75 147.67 7.92
2007 Sasha Land 138.94 146.54 7.60
2006 Hans Arnesen 137.68 146.80 9.12
2005 Hans Arnesen 135.70 146.81 11.11
2004 Chris Vargo 141.49 150.03 8.54
2003 John Grant 143.38 150.98 7.60
2002 Luke Isenbarger 145.00 151.60 6.60
2001 Josh Beatty 149.87 159.93 10.06
2000 Chris Wojtowich 144.28 154.38 10.10

Interestingly, Young does not hold the fastest average adjusted time. This honor goes to Hans Arnesen, whose blazing 135.70 was 11 seconds faster than the top 50 average.

This suggest that while Young was undoubtedly the fastest rider last Wednesday, the gap between the fastest rider and the field is not as large as we saw in 2005.

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