Uses python3.6
In order to obtain the entire 2017 KBO baseball data, simply run the following.
from scrape import *
year = '2015'
months = ['03', '04', '05', '06', '07', '08', '09', '10']
summaries = []
for month in months:
summaries += MatchSummaryParser(year, month).parse()
matches = []
for summary in summaries:
try:
matches.append(
MatchDetailParser(
summary.year,
summary.month,
summary.day,
summary.get_away_team_name(),
summary.get_home_team_name()
).parse()
)
except DetailDataNotFoundException:
# this is most likely a double header game.
passsummary.r : 점수
summary.b : 볼넷 + 사사구
summary.e : 팀 실수
summary.h : 안타
pitcher_info.at_bats : 상대한 타자 수
pitcher_info.hits : 맞은 안타 수
pitcher_info.bbhp : 던진 볼넷 + 사사구
pitcher_info.home_runs : 맞은 홈런 수
pitcher_info.strike_outs : 잡은 스트라이크 아웃 수
pitcher_info.at_bats : 상대한 타자 수
pitcher_info.scores_lost : 투수가 내준 점수
pitcher_info.errors : 투수 실수
pitcher_info.era : 투수 방어율
pitcher_info.innings : 던진 이닝 수. 단, 내림 처리 하였기 때문에 3 1/2, 2/3 모두 3으로 내림.
pitcher_info.wins : 투수의 시즌 승수
pitcher_info.loses : 투수의 시즌 패배 수
pitcher_info.saves : 투수의 시즌 세이브 수
pitcher_info.num_balls_thrown : 투수가 해당 경기 던진 공 수
pitcher_info.game_count : 투수의 시즌 등판 수
batter_info.at_bats : 타자가 해당 경기에 타석에 들어간 횟 수
batter_info.hits : 타자의 해당 게임 안타 수
batter_info.hra : 타자의 해당 경기 종료 시점의 타율
batter_info.rbi : 타자의 해당 경기 타점 (때려서 홈으로 불러 들여온 주자 수)
batter_info.runs : 타자의 해당 경기 득점 (본인이 홈을 밟은 수)
team_standing.draws : 종료 시점의 해당 팀의 무승부 수
team_standing.era : 종료 시점의 팀의 평균 방어율
team_standing.hra : 종료 시점의 팀의 평균 타율
team_standing.wra : 종료 시점의 팀의 평균 승률
team_standing.wins : 종료 시점의 팀의 승수
team_standing.loses : 종료 시점의 팀의 패수
team_standing.rank : 종료 시점의 팀의 랭킹
some specific game's data (with some pitchers and batters omitted for brevity)
{
"year": "2017",
"month": "03",
"day": "14",
"away_team_name": "KT",
"home_team_name": "SAMSUNG",
"score_board": {
"scores": {
"away": [
3,
1,
1,
0,
1,
0,
2,
0,
1
],
"home": [
0,
0,
0,
0,
1,
0,
0,
0,
0
]
},
"summary": {
"away": {
"r": 9,
"b": 6,
"e": 0,
"h": 12
},
"home": {
"r": 1,
"b": 3,
"e": 0,
"h": 7
}
}
},
"pitcher_info": {
"home": [
{
"at_bats": 11,
"hits": 6,
"bbhp": 3,
"home_runs": 0,
"strike_outs": 1,
"scores_lost": 5,
"errors": 5,
"era": "15.00",
"name": "\ucd5c\ucda9\uc5f0",
"innings": "3",
"wins": 0,
"loses": 1,
"saves": 0,
"num_balls_thrown": 60,
"game_count": 1
}
],
"away": [
{
"at_bats": 18,
"hits": 6,
"bbhp": 1,
"home_runs": 0,
"strike_outs": 1,
"scores_lost": 1,
"errors": 1,
"era": "1.80",
"name": "\ub85c\uce58",
"innings": "5",
"wins": 1,
"loses": 0,
"saves": 0,
"num_balls_thrown": 72,
"game_count": 1
}
]
},
"batter_info": {
"home": [
{
"at_bats": 2,
"hits": 1,
"hra": "0.500",
"rbi": 1,
"runs": 0,
"name": "\ubc15\ud574\ubbfc"
}
],
"away": [
{
"at_bats": 3,
"hits": 1,
"hra": "0.333",
"rbi": 0,
"runs": 1,
"name": "\uc774\ub300\ud615"
}
]
},
"away_team_standing": {
"draws": 0,
"era": 5.53,
"hra": 0.264,
"wra": 0.373,
"wins": 25,
"loses": 42,
"rank": 9,
"name": "KT"
},
"home_team_standing": {
"draws": 2,
"era": 5.81,
"hra": 0.265,
"wra": 0.369,
"wins": 24,
"loses": 41,
"rank": 10,
"name": "SAMSUNG"
}
}
]The prediction model employs the notion of Deep Learning to predict the result from the data.
The scraped data is parsed in the formatter.py to follow the below format:
hometeam's data = The summary + standing of the team's previous k games # k is the usr input.
awayteam's data = The summary + standing of the team's previous k games
result of the game = [x, y]
The builder.py constructs the neural network that takes in the above format of data to be trained and predict the result of the game.
When taking the individual team data, as we are not distinguishing the team specific statistics, auto encoder & decoder were used to get rid of such uncertainty then passed on to neural network.
The number of layers can't be decided by the user yet but we are planning on letting the user take care of such details.
- Each layer of neural network will perform dropout according to the usr input.
- SeLU and AdamOptimzer were used in the architecture.
The SELU implementation taken from https://github.com/bioinf-jku/SNNs/blob/master/selu.py
To run the code you are required to use python3. I suggest following the below instruction to run the code.
virtualenv -p python3 .env
source .env/bin/activate
pip install -r requirements.txt
# Run the code
deactivate # Exit the virtual environment
After activating the virtual envelop, run
python trainer.py -h
# Result
KBO Score Prediction Trainer
positional arguments:
year The year of data to train the model with
train_size The proportion of the training set to the test set
model_name The name of the model
learn_rate The learning rate
sequence_length Sequence length
epoch Training epoch
drop_rate Drop rate
optional arguments:
-h, --help show this help message and exit
to see what the user needs to provide.
The model takes in 7 inputs from the user.
- The year of the game to train the Deep Learning model with.
- The proportion of the training set to the test set in the raw dataset.
- The name of the model. (For saving purpose)
- The learning rate of the model.
- The number of previous games the model should look at for each game.
- The iteration of training.
- The drop rate.
This is done automatically when ran train.py. The future use is to call the specific model in the future when we want to predict the result. We are still working on the frontend of the prediction model.
Data scraping: Nate Namgun Kim nk15@rice.edu
Prediction model: Jay JungHee Ryu jr51@rice.edu