[PYTHON] It seems that a new lottery with a total score will start in the sports lottery,

background

• Sports lottery is one of the businesses operated by the Japan Sport Council (JAPAN SPORT COUNCIL) and is sold to obtain funds for "sports promotion". Japan Sport Council: Sports Promotion Voting Business
• "MEGA BIG", which exceeds the maximum winning amount of 600 million yen in the sports lottery, "MEGA BIG" will be released from this month.
• "BIG" is for 14 games with wins, divisions and losses, and "MEGA BIG" is for 12 games with a total score of 1 point or less, 2 points, 3 points and 4 points or more.

• Beyond the desperate "BIG", it is "MEGA BIG" that does not even describe it.

Main subject

• What is the distribution of total scores in J-League games?
• 6,130 games were targeted in the six years from 2014 to 2019, when the J3 League began.
• The breakdown is J1: 1,836 games, J2: 2,772 games, J3: 1,522 games.

Process flow

1. Get data from the schedule / results page of Jleague Data Site from 2014 to 2019
2. Read the CSV file obtained in 1) and create a DataFrame.
3. In addition, create a separate DataFrame for J1, J2, and J3 only.
4. Create 4 graphs with matplotlib.
5. Aggregate the "total score" to 1 point or less, 2 points, 3 points, 4 points or more of "MEGA BIG".
6. Graph the aggregated results.

code

① Get data from the schedule / result page of Jleague Data Site from 2014 to 2019

(2) Read the CSV file obtained in (1) and create a DataFrame.

col_name = ['year','Tournament','section','Match day','K/O time','home','Score','Away','Stadium','Visitors','TV broadcast']
results = pd.DataFrame(index=[], columns=col_name)

for f in files:
    tmp_data = pd.read_csv(f, sep=',', encoding='utf-8')
    results = results.append(tmp_data, ignore_index=True, sort=False)

③ In addition, create DataFrame separately for J1, J2, and J3 only.

#Total score of data for J1, J2, and J3 only
score_J1 = score_data[score_data['Tournament'] == 'Ｊ１']
idx_J1 = sorted(score_J1['Total score'].unique())
scoreJ1 = pd.DataFrame({'Total score':idx_J1, 'cnt':score_J1['Total score'].value_counts()}, index=idx_J1)
scoreJ1 = scoreJ1.reset_index().drop('index', axis=1)

score_J2 = score_data[score_data['Tournament'] == 'Ｊ２']
idx_J2 = sorted(score_J2['Total score'].unique())
scoreJ2 = pd.DataFrame({'Total score':idx_J2, 'cnt':score_J2['Total score'].value_counts()}, index=idx_J2)
scoreJ2 = scoreJ2.reset_index().drop('index', axis=1)

score_J3 = score_data[score_data['Tournament'] == 'Ｊ３']
idx_J3 = sorted(score_J3['Total score'].unique())
scoreJ3 = pd.DataFrame({'Total score':idx_J3, 'cnt':score_J3['Total score'].value_counts()}, index=idx_J3)
scoreJ3 = scoreJ3.reset_index().drop('index', axis=1)

④ Create 4 graphs with matplotlib.

#Graph J1, J2, J3, and the whole
fig = plt.figure(figsize=(16,9),dpi=144)
fig.subplots_adjust(hspace=0.4)

#Original graph style settings
plt.style.use("mystyle")
plt.rcParams["font.family"] = "IPAexGothic"

#For storing graph objects
axes = []
score_list = [scoreJ1['Total score'], scoreJ2['Total score'], scoreJ3['Total score'], score_all['Total score']]
cnt_list = [scoreJ1['cnt'], scoreJ2['cnt'], scoreJ3['cnt'], score_all['cnt']]
cat_list = ['J1', 'J2', 'J3', 'ALL']

#Loop through 4 graphs of J1, J2, J3, ALL
for i in range(4):
    axes.append(fig.add_subplot(4,1,i+1))
    axes[i].bar(score_list[i], cnt_list[i])
    [axes[i].text(score_list[i][s], cnt_list[i][s]+25, str(score), size=12, color='r', ha='center') for s, score in enumerate(cnt_list[i])]
    axes[i].set_xticks(np.arange(0,16,1))
    axes[i].set_ylabel(cat_list[i])
    axes[i].set_ylim(0,1500)
    axes[i].text(15-1, 1500-200, 'n:'+str(sum(cnt_list[i])))

plt.xlabel('Total score')

txt1 = 'I tried to visualize the total score of the match in the J League.'
fig.text(.05, .9, txt1, fontsize=32, horizontalalignment="left")
txt2 = "Source: JLeague Data Site"
fig.text(.9, .05, txt2, fontsize=14, horizontalalignment="right")

plt.savefig('./img/score.png')
plt.show()

⑤ Add "Mega" column to "Total score" with 1 point or less, 2 points, 3 points, 4 points or more of "MEGA BIG"

#Make MEGA score classification
def mega(df):
    if df in (2, 3):
        return df
    elif df <= 1:
        return 1
    elif df >=4:
        return 4

score_data['Mega'] = score_data['Total score'].apply(mega)

⑥ Make a graph of the aggregated results.

Summary

• In any league, the cumulative total of 0 points, 1 point, 2 points, and 3 points is about 75% of the total.
• Rather than using 5 types of 0 points, 1 point, 2 points, 3 points, and 4 points or more, it seems that the appearance rate was averaged and 0 points and 1 point were set to 1 to make 4 types. (Guess)
• There was little technical sharing, but fact sharing was the main focus.
• The amount of code can be reduced by looping multiple drawings to for in the graph and inclusion notation.
• Unfortunately, the sports lottery is said to have become even more ** "donation without reward" **.