2015 NBA Attendance Analysis

Now that the NBA regular season is in the books, let's take a look at how home attendance has changed (or not changed) over the last two seasons using the NBA attendance numbers from ESPN.  So specifically I want to know if average regular season home attendance has changed from the 2013/14 season to the 2014/15 season statistically.  I choose to use a t-test (assuming unequal variance) in NBA teams average home regular season attendance.  What I find is exactly what I found previously, and that is that NBA average home regular season attendance has not changed in a statistical sense.  In this case, the value of the t-test for NBA average home regular season attendance between the two seasons is 0.215, which is greater than generally accepted confidence level of 0.05.  Hence, I conclude that NBA average home regular season attendance in the 2014/15 season is statistically no different than in the 2013/14 season.

2015 NHL Attendance Analysis

Now that the NHL regular season is over, let's take a look at NHL team attendance and see how fan attendance compares over the last two NHL seasons and see if anything is different from earlier analysis I have done for NHL average home regular season attendance.

First, I downloaded the NHL attendance data from ESPN for the last two NHL regular seasons, sorted the data and then calculated the t-test for the last two NHL regular seasons and found that there is no statistical difference between the home regular season attendance in the two seasons since the partial lockout.

For those curious, the t-test was 0.406 comparing the lockout season and the 2013/14 NHL season using a two sample equal (and unequal) variance measure and the t-test was 0.445 comparing the 2013/14 and 2014/15 NHL season using a two sample equal (and unequal) variance measure.

2014-15 NHL Competitive Balance

With the 2014-15 NHL regular season in the books, let's take a look at how competitively balanced the season was (using the Noll-Scully measure of competitive balance) and compare this with recent seasons.

There are two ways of measuring competitive balance in hockey since unlike baseball or basketball, hockey games can end up tied at the end of regulation.  So I will report both the binomial and the trinomial Noll-Scully measure.  Additionally, there are two ways of reporting both the binomial and trinomial Noll-Scully measure:  one using the standard deviation of a sample and the other using the standard deviation of the population.  Again, I will report both.

Additionally, I will have to compute (for the trinomial distribution) the probability of a tie under equal playing strength.  In the past, I used Richardson's estimate from Stanley Cup playoff games.  In this case I will change and just assume that the probability of games that go into overtime occurs among teams with equal playing strength.  Feel free to quibble with this, as this is simplification of the estimated probability.

OK, with the measurement details noted, here are the Noll-Scully competitive balance numbers for the recent NHL season.  Using the sample the Noll-Scully under the binomial distribution was 1.5634 and under the trinomial distribution was 2.0286.  For the population the Noll-Scully under the binomial distribution was 1.5372 and under the trinomial distribution was 1.9945.

Compared to recent years, this year was less competitive balanced, but overall the level of competitive balance in the NHL is still rather similar to recent historical numbers.

2014 MLB Player Production with Runs Created

In the previous post I looked at MLB team runs created productivity.  While the model does an excellent job of explaining how runs are created, here I want to focus on the individual batters for the 2014 season and look at their individual contributions to the team's runs.

To do that, I take the estimated coefficients from the MLB team Runs Created model and multiply them by the actual player statistics for each player in 2014.  Calculating this yields an value of the players Runs Created.  (If you are interested, I have blogged previously on how to calculate MLB Player Production using MLB Team Runs Created).

So, for the past season, the most productive player in terms of Runs Created was Mike Trout.  The top 10 players for 2014 are listed in the table below.

playerID		teamID		Runs Created
troutmi01		LAA		127.23
mccutan01		PIT		116.21
bautijo02		TOR		113.96
cabremi01		DET		112.95
martivi01		DET		112.61
brantmi02		CLE		111.68
abreujo02		CHA		110.99
altuvjo01		HOU		107.00
stantmi03		MIA		105.63
cruzne02		BAL		104.04

For comparison purposes, here is the link for MLB player Runs Created in 2012 and 2013.

2014 MLB Team Runs Created Productivity

In my Sports Economics course we look at how to estimate the productivity of MLB players.  In order to do that, we first look at estimating the productivity of MLB teams using a model created by Asher Blass that was published in 1992.  Almost every year I update his model using MLB data from Sean Lahman's database.  So I did the MLB Team Runs Created analysis (step-by-step procedure to estimate MLB Team Runs Created) for the 2014 season, and here are the results.  In step 8, I talk about the statistical analysis called a linear regression adjusting for heteroskedasticity, which is what I am reporting below.

Here are the estimated results for these 15 seasons of data (2000-2014).

Variable	Coefficient	Std. Error	t-Statistic	Prob.
SINGLE	0.499	0.02	24.87	0.00
DOUBLE	0.718	0.04	16.86	0.00
TRIPLE	1.137	0.12	9.32	0.00
HR	1.463	0.04	37.62	0.00
NBB	0.288	0.02	14.31	0.00
SB	0.103	0.04	2.61	0.01
GDIPCS	-0.028	0.06	-0.48	0.63
HBP	0.398	0.08	4.88	0.00
SF	0.774	0.17	4.63	0.00
OUTS	-0.102	0.02	-4.19	0.00
C	-10.644	106.03	-0.10	0.92

A few observations: first, other than grounded into double plays and caught stealing (GIDPCS) (along with the constant term) each of the variables is statistically significant at the 99% confidence level and of the correct sign. Only OUTS are negative and statistically significant. Second,the coefficient on HR's is greater than the coefficient on Singles, which means that a HR will on average generate more runs than a single. All the other coefficients seem to make sense as well.

For comparison purposes, here is the blog for 2013 MLB Team Runs Created.

2014 NCAA FBS Competitive Balance

Now that the 2014 season is in the books, I want to take a short look at competitive balance in NCAA football.  For more background read my previous analysis of competitive balance in NCAA football. What I want to do in this blog is simply update the data over the past two seasons in terms of competitive balance.  Using the Noll-Scully measure of competitive balance, I find that NCAA football was 1.43 in 2014, which is more balanced than the 1.65 in 2013.  Yet, overall, NCAA football does not seem to be much different in terms of competitive balance from year to year as you can see in the table that I give in the blog linked above.

2014 NCAA FBS Final Ranking

Ohio State is the winner of the first bowl subdivision championship game! So now that all of the post season football games are finished, I have run the Complex Invasion College Football Production Model one last time for this season using the data from cfbstats.com.  Unlike the last few years, where the team ranked the highest in the model was also the "National Champion", this season Ohio State ranks as the #4 most productive team behind #1 Texas Christian, #2 Oregon (the runner-up in the championship game) and #3 Michigan State.  The highest ranked SEC team is #7 Alabama.

Rank	Team
1	TCU
2	Oregon
3	Michigan State
4	Ohio State
5	Marshall
6	Baylor
7	Alabama
8	Georgia
9	Louisiana Tech
10	Wisconsin
11	Clemson
12	Memphis
13	Boise State
14	East Carolina
15	Georgia Tech
16	Mississippi
17	Georgia Southern
18	Mississippi State
19	Louisville
20	Stanford
21	BYU
22	Arkansas
23	Northern Illinois
24	Houston
25	North Carolina State

Previous 2014 NCAA FBS Top 25 Rankings
2014 NCAA FBS Top 25 for Week 16
2014 NCAA FBS Top 25 for Week 15
2014 NCAA FBS Top 25 for Week 14
2014 NCAA FBS Top 25 for Week 13
2014 NCAA FBS Top 25 for Week 12
2014 NCAA FBS Top 25 for Week 11
2014 NCAA FBS Top 25 for Week 10
2014 NCAA FBS Top 25 for Week 9
2014 NCAA FBS Top 25 for Week 8
2014 NCAA FBS Top 25 for Week 7
2014 NCAA FBS Top 25 for Week 6
2014 NCAA FBS Top 25 for Week 5
2014 NCAA FBS Top 25 for Week 4
2014 NCAA FBS Top 25 for Week 3
2014 NCAA FBS Top 25 for Week 2