1. NCAA Basketball Tournament: Predicting Performance
Doug Fenton, Ben Nastou,
Jon Potter

2. Project Goals
To examine some of the factors that indicate how well a team will do in the NCAA Men’s B-Ball Tournament
To compare factors (such as seed, conference, and individual team) and their effect on a team’s result
To create an effective model for how well a team does based on certain factors

3. Background
The NCAA Basketball Tournament is a 64-team, single elimination tournament.
This has been the tournament’s format since 1985: we use data from
There are four separate regions, each with sixteen teams seeded 1-16 (with 1 being the best and 16 being the worst).
The result (dependent) variable is based on how many games a team wins in the tournament.

4. General Analysis Result =.599 - 0.151*seed + 2.32*percent
The first two (independent) variables looked at were the team’s seed and its winning percentage.
The regression was as follows:
Result = *seed *percent
(R^2=.387) (1.87) (-18.43) (6.00)
As can be seen from this data, both seed and winning percentage had a large effect on the team’s result, with result being positively related to percent and negatively related to seed.

5. A Quick Look at the Seeds...

6. #1 Seed vs. #2 Seed We can therefore conclude with 95% certainty
Histogram for #1 Seed
Histogram for #2 Seed
Assuming normality in the distribution of results for #1 Seeds and #2 Seeds,
T n+m-2 = (Avg(x)-Avg(y))/(SP*(sqrt(1/n+1/m)) = 3.46
T n+m-2 = 3.46 > 1.64 = T 0.05, 126
We can therefore conclude with 95% certainty
that One Seeds outperform Two Seeds.

7. Differences in Variance
Do #1 Seeds have a different variance from #2 Seeds?
H0: S12 = S vs H1: S12 ≠ S22
F = S12 / S22 = 2.49/2.22 = 1.12
Critical Value: F(63, 63) with 95% confidence:
.600<F<1.67
Therefore, the F-stat falls within the interval and #1 Seeds may have the same variance as the #2 Seeds.

8. Seed Analysis
With other seeds, the result data cannot be assumed to be normal.
Therefore, hypothesis testing comparing seeds could not be used
However, we were able to test if the probability of a given seed winning the championship was different than 1/16 (If all seeds were created equal).

9. Does the Top Seed Win the Championship More Often?
H0 : p1 = 1/16 vs. H1 : p1 > 1/16
t = (9 - 16*(1/16)) / ((16*1/16(1-1/16))^.5)
t = 8.26 > t(.05,15) = 1.74
Therefore, we reject the null hypothesis, which means that the top seed wins the championship more often than if the tournament was randomly seeded.

10. Do the High Seeds Typically Outperform the Lower Seeds
| Hi Seeds (1-8) | Lo Seeds (9-16) | Total
Lo Result(0-2) | | | 896
Hi Result (3-6) | | | 128
Total | | | 1024
% Hi Result | |
H0: pH = pL vs H1: pH > pL
Phat = (120+8)/( ) = .125
Z = (see Thm ) = > 1.64 = Z.05
Hence, as expected, higher seeds outperform lower seeds.

11. The Conference Variables
The teams from our study came from 31 different conferences.
These conferences were divided into 4 different tiers based past tournament performance and the number of schools who get into the tournament each year (Tier 1 being strongest conferences; Tier 4 being weakest conferences)
We then tested how a team’s conference tier was correlated with their performance.

12. Comparing Teams’ Conferences
We tested the correlation between a team’s conference tier and their performance in the tournament.
Likewise, we tested to see if there was significance of a team’s winning percentage given their conference tier.
Therefore, we created a dummy variable for each tier and interaction terms between tier and winning percentage.

13. Results (R^2 = .40) result | Coef. Std. Err. t P>|t|
win % |
Tier 1 |
Tier 2 |
Tier 3 |
%*T1 |
%*T2 |
%*T3 |

14. Is Tournament Fairly Seeded Based on Conference Tier?
To see if this is true, we looked at only the top 4 seeds because they seemed the most normal.
For each of these seeds, we created four groups, one for each tier; to see if performance was consistent with the conference tier given a team’s seed.
ANOVA was used for analysis of:
H0: MT1 = MT2 = MT3 = MT4 (for each seed 1-4)

15. Results --------------------------------------------------
Seed Group | F | F-critical
Seed | |
Seed | |
Seed | |
Seed | |

16. Analyzing Certain Teams
Dummy Variables were created for teams which had been in at least 12 (75%) of the tournaments.
There are not enough data points, and the histograms are too skewed, to assume normality for the team data

17. A Quick Look at the Teams’ Performances

18. Is Duke the Best? Duke vs. Kentucky
Assuming normality in the distribution of results for both
Kentucky and Duke (which may not be a valid assumption),
T n+m-2 = (Avg(x)-Avg(y))/(SP*(sqrt(1/n+1/m)) = 0.356
T n+m-2 = < = T 0.20, 26
Therefore, we cannot reject the null hypothesis that Duke and Kentucky have perform equally well with even 20% certainty

19. Time Trends?
According to this time trend regression, Kentucky would have overtaken Duke in 1994.

20. Maybe So...

21. Are Certain Teams Mis-Seeded?
If the team’s dummy variable is significant with seed, it suggests that that team is often “mis-seeded” (ie. a team is consistently seeded higher or lower than it should be).

22. Under-Rated?
So, for example, Duke can be expected to win more than one more game than other teams of the same seed, and Illinois can be expected to win more than half a game less than other teams of the same seed.
If Duke and Illinois are seeded the same, Duke can be expected to win almost two full games more than Illinois.

23. Analyzing Experience
An experience variable was created to reflect the total number of previous tournament games (won or lost) a team had played since 1985.
Result = *experience *year
(R^2=.14) (12.80) (12.87) (-5.51)
Hence, there is correlation between experience and result, suggesting that teams which have been in the tournament often typically win more games… also, successful teams typically stay successful.

24. Regression with Experience (R^2=.39)
result | Coef. Std. Err t P>|t|
win % |
seed |
exper |
year |

25. Experience (cont…)
That experience is significant when regressed with seed and winning percentage indicates that it is not fully accounted for in the seeding of teams, and that it is another variable worth looking at when making tournament predictions.
The experience variable is significant in a variety of regressions indicating its robustness as an explanatory variable

26. FINAL REGRESSION

27. Conclusions
Tournament predictions can be fairly accurate based solely on seed
There are other predictors such as winning percentage, conference, and experience which can be used to refine predictions
However, better teams don’t always win, so it is impossible to make predictions absolutely