Correlation between Winning Percentage in Home Stadiumand Environmental and Crowd Pressure

１.Introduction

The concept of home advantage exists in competitive sports matches, where the hometeam is said to be more likely to win than the away team. The possible causes of this are as
follows①Familiarity with the natural environment (temperature, humidity)②Familiarity with the facility environment (venue equipment)③Differences in the amount of support due to differences in the number of spectators.
Of these, it is difficult to determine the causes of ② familiarity with the facility environment
from the statistics presented here, so we will investigate the causes of ① familiarity with
the natural environment and ③ differences in the amount of cheering due to differences in
the number of spectators. However, in this study, we will assume that the number ofspectators in ③ is larger for the home team and that the ratio is constant.

２. Methods/Results④Visualizing Correlation

In section ④, we visualized the data on correlation obtained in section ③ using scatterplots. The scatter plots of the 11 teams are shown below.
(Note: The horizontal axis represents the number of spectators (in thousands), humidity(10%), and temperature (°C), and the vertical axis represents 0 as a loss and 1 as a win.

Sendai 89ERS

1.5110.5

0.500①Proving Home Advantage

1.5

It is said that a home advantage exists, but we used a t-test to demonstrate whether thisdifference actually exists.
Niigata
Nishinomiya
Chiba
AomoriSendai
Kawasaki
Osaka
ShimaneTokyo.E
Tochigi
HachiojiToyamaFukushima
Koshigaya
Yokohama
Kyoto
Kanazawa
Kumamoto
Gunma
Hiroshima
KagawaSannenn
Yamagata
ShigaAkita
Sinnsyu
A.Tokyo
RyukyuF.NagoyaTokyo.Z
Shibuya
MikawaNaraFukuoka
Hokkaido
Ehime
Ibaraki
Utsunomiya
Nagoya.D
AverageT-testWinning at home(％) Winning at away(％) ease of winning

57.69
69.81
76.79
29.63
78.43
72
55.77
52.94
47.83
78.13
16.67
54
46.3
26.92
28.85
52
40
59.32
67.92
64.81
45.28
28.57
32.14
48.08
44
86.21
78.43
71.92
61.11
28.3
60.78

61.53
42.59
19.6
23.52
41.5
67.92
77.77
48.97
52.411085
0.0022791

54.90 home
48.15 home
83.67 away
37.74 away
55.36 home
65.38 home
40.82 home53.7 away
41.67 home
81.25 away
20 away
58.49 away
33.96 home38.1 away
21.15 home
49.02 home
30 home58.49 home
72.88 away
69.81 away
40.74 home
26.92 home
23.53 home
28.57 home
27.45 home
79.25 home73.21 home
58 home62.26 away
35.18 away
46 home

The table on the left shows the annual winning
percentage of each team in the 2018-2019 B-League B1
when they played at their home stadium (henceforthreferred to as home) and when they played at theiraway stadium (henceforth referred to as away). At thebottom of the table is the average of each team's home
and away winning percentages.
As a result of finding the mean, it was found thathome winning percentage > away winning percentage,
which means that the team is more likely to win at
home. Using the t-test, it was determined that thedifference between the home and away winning
percentages was valid, as the significant difference was
p=0.002<0.05.

Using a t-test, we were able todetermine that a home advantage
exists.

34.69 home
32.07 home
25 away
25 away
40.74 home46.29 home
77.27 home
49.09 away
47.328255②Selection of the team with the highest winning
percentage in its home stadium
Nishinomiya
Sendai
Osaka
Fukushima
ShigaAkita
RyukyuShibuya
MikawaNaraIbaraki

Winning at home(％)

69.81
78.43
55.77
46.3
48.08
44
71.92
60.78
61.53
42.59
67.92

Winning at away(％)

48.15
55.36
40.82
33.96
28.57
27.45
58
46
34.69
32.07
46.29

In the data used in this study, not allteams had a higher winning percentage
at home. Therefore, we determined thehome and away winning percentages of
all teams respectively, and examined
the teams whose home winning
percentage was more than 10% greaterthan their away winning percentage. As
a result, we were able to select 11teams.

③Correlation between natural environment(temperature, humidity), attendance and winning
percentage

Method ③ aimed to investigate ① familiarity with the natural environment and ② the sizeof the spectators cheering as described in the Introduction. Therefore, we took thenumerical values that show the correlation between the values of win/loss, temperature,humidity, and number of spectators for the games played by the 11 teams selected in
Method ① as their home teams.(The table below shows the correlation values for eachteam.Translated with www.DeepL.com/Translator (free version)

Nishinomiya
Sendai
Osaka
Fukushima
Shiga
Akita
RyukyuShibuya
MikawaNara
Ibarakitemperaturehumidity
number of spectators-0.006-0.0780.168
0.056-0.045-0.130-0.1100.057-0.159-0.189-0.076-0.105-0.132-0.001-0.6390.109-0.1200.007-0.211-0.131-0.092-0.161-0.181-0.176-0.0860.045-0.0770.237
0.151-0.269-0.014-0.015
0.063

10-102030

4050

Akita Northern Happinets

01

10.5
0102030405060

Ryukyu Golden Kings

1.501

10.5
0510152025303540

Bambishas Nara

1.501

10.5
05

1015202530

Sunrockers Shibuya

1.50

1

10.5
0

1020304050015202530

1020304050

Ibaraki Robots

0102030

40

50

60

2530

Nishinomiya Stokes

05101520

Shiga Lake Stars

05

1015202530

3540

Osaka Evessa

1.5
1number of spectators

0.500

1.5

0.5
010Seahorse Mikawa

1.50.505

1.50.500

1.50.50

Fukushima Firebonds

1.50102030

40506070

temperaturehumidity

80

From the above graph, it was easy to see that there was no correlation in
the data, as there were no characteristics such as high attendance when
winning and high humidity when losing. Also, there was no regularity inthe variance of the data.

３.Consideration

２．What we were able to learn from the methods and results
From ①, home advantage actually exists.
From ③④, since there is only one number with a correlation of 0.5 or higher or -0.5 or lower,
there is no correlation between the number of spectators, temperature, humidity, and thenumber of games won or lost.

４. Summary and Future Prospects

In this study, from the investigation of ① proof of home advantage, ② selection of teams
with high winning percentages in their home stadiums, ③ correlation between naturalenvironment (temperature, humidity), number of spectators and winning percentages, and④ visualization of the correlation, it was found that there is no correlation between weather
conditions, number of spectators and winning percentages in professional basketball games.
In addition, there are some points to reflect on in this research.
・We did not have a breakdown of the number of spectators (the number of people
supporting each team).
・We were only able to use one year's worth of data, so the overall results lacked credibility.
In future research, we would like to improve on the above points, investigate the causes
from different perspectives and items that we were not able to examine this time, andactively analyze teams that are stronger on the road.

５. Acknowledgments

We would like to express our gratitude to the Research Center for Medical and Health Data
Science, The Institute of Statistical Mathematics, National Institute of Information andSystems, Japan Professional Basketball League, and Data Stadium Corporation for providing
the data for this research.