# トレバー・バウアー(Trevor Bauer) の研究① 

**Published by:** [Shogaku](https://paragraph.com/@shogaku/)
**Published on:** 2023-04-13
**URL:** https://paragraph.com/@shogaku/trevor-bauer

## Content

ローカルライン京急線にバウアー(Trevor Bauer)が現れた、ということでとても興味が出てきたのでバウアーについて勉強した。 Part1 とりあえず、データを見る https://baseballsavant.mlb.com/savant-player/trevor-bauer-545333データ取得2019～2021のデータを見る!pip install pybaseball from pybaseball import statcast import pandas as pd import pandas as pd from pybaseball import statcast dates = [ '2021-04-02', '2021-04-07', '2021-04-13', '2021-04-18', '2021-04-24', '2021-04-29', '2021-05-04', '2021-05-09', '2021-05-15', '2021-05-21', '2021-05-26', '2021-05-31', '2021-06-06', '2021-06-12', '2021-06-18', '2021-06-23', '2021-06-28', '2020-07-26', '2020-08-02', '2020-08-07', '2020-08-19', '2020-08-24', '2020-08-29', '2020-09-04', '2020-09-09', '2020-09-14', '2020-09-19', '2020-09-23', '2019-03-30', '2019-04-04', '2019-04-10', '2019-04-15', '2019-04-20', '2019-04-25', '2019-04-30', '2019-05-06', '2019-05-11', '2019-05-16', '2019-05-21', '2019-05-26', '2019-05-31', '2019-06-06', '2019-06-11', '2019-06-16', '2019-06-21', '2019-06-26', '2019-07-02', '2019-07-07', '2019-07-13', '2019-07-18', '2019-07-23', '2019-07-28', '2019-08-03', '2019-08-09', '2019-08-14', '2019-08-19', '2019-08-25', '2019-08-31', '2019-09-04', '2019-09-10', '2019-09-15', '2019-09-22' ] # Create an empty DataFrame to store the data df_545333_all_dates = pd.DataFrame() # Fetch data for each date and concatenate for date in dates: df_single_day = statcast(start_dt=date, end_dt=date) df_545333_single_day = df_single_day[df_single_day['pitcher'] == 545333] df_545333_all_dates = pd.concat([df_545333_all_dates, df_545333_single_day]) # Reset the index of the final DataFrame df_545333_all_dates.reset_index(drop=True, inplace=True) 球種確認# 投球結果を抽出 df_545333 = df_545333_all_dates # df_545333のpitch_typeカラムに含まれるユニークな球種を確認する unique_pitch_types = df_545333['pitch_type'].unique() # 確認した球種を表示する print(unique_pitch_types) 結果['FC' 'FF' 'ST' 'KC' 'CH' 'SI' nan] FC: カットファストボールFF: フォーシームファストボールST: シンカー（ファストボール）KC: ナックルカーブCH: チェンジアップSI: 2シームファストボール各年の球種import pandas as pd def pitch_counts(df): # 左打者と右打者に対する投球データを抽出 df_L = df[df['stand'] == 'L'] df_R = df[df['stand'] == 'R'] # 各カテゴリーでの球種の出現回数をカウント total_counts = df['pitch_type'].value_counts() left_counts = df_L['pitch_type'].value_counts() right_counts = df_R['pitch_type'].value_counts() # 出現回数をデータフレームにまとめる pitch_counts_table = pd.DataFrame({'Total': total_counts, 'Left Batter': left_counts, 'Right Batter': right_counts}) # NaNを0に置き換える pitch_counts_table.fillna(0, inplace=True) # カウントを整数に変換する pitch_counts_table = pitch_counts_table.astype(int) return pitch_counts_table # 続けて、球種カウントのコードを実行します。 # Split the data by year df_2019 = df_545333_all_dates[df_545333_all_dates['game_year'] == 2019] df_2020 = df_545333_all_dates[df_545333_all_dates['game_year'] == 2020] df_2021 = df_545333_all_dates[df_545333_all_dates['game_year'] == 2021] # Get pitch counts for each year pitch_counts_2019 = pitch_counts(df_2019) pitch_counts_2020 = pitch_counts(df_2020) pitch_counts_2021 = pitch_counts(df_2021) # Display the results print("2019年の球種カウント:") print(pitch_counts_2019) print("\n2020年の球種カウント:") print(pitch_counts_2020) print("\n2021年の球種カウント:") print(pitch_counts_2021) 各年の球種(円グラフ)フォーシームファストボールが多いピッチャーであることがわかるimport matplotlib.pyplot as plt def plot_pitch_distribution(df, year): df_L = df[df['stand'] == 'L'] df_R = df[df['stand'] == 'R'] fig, axs = plt.subplots(1, 3, figsize=(18, 6)) plt.suptitle(f'Pitch Distribution in {year}') colors = {'FC': 'red', 'FF': 'blue', 'ST': 'green', 'KC': 'orange', 'CH': 'purple', 'SI': 'brown'} # Total df['pitch_type'].value_counts().plot(kind='pie', ax=axs[0], autopct='%.1f%%', colors=[colors[key] for key in df['pitch_type'].value_counts().index]) axs[0].set_title('Total') axs[0].set_ylabel('') # vs Left batter df_L['pitch_type'].value_counts().plot(kind='pie', ax=axs[1], autopct='%.1f%%', colors=[colors[key] for key in df_L['pitch_type'].value_counts().index]) axs[1].set_title('vs Left batter') axs[1].set_ylabel('') # vs Right batter df_R['pitch_type'].value_counts().plot(kind='pie', ax=axs[2], autopct='%.1f%%', colors=[colors[key] for key in df_R['pitch_type'].value_counts().index]) axs[2].set_title('vs Right batter') axs[2].set_ylabel('') plt.show() # Plot pitch distribution for 2019, 2020, and 2021 data plot_pitch_distribution(df_2019, 2019) plot_pitch_distribution(df_2020, 2020) plot_pitch_distribution(df_2021, 2021) 各年の投球コース (散布図)真ん中の薄い赤色は、ざっくりストライクゾーンですimport matplotlib.pyplot as plt def plot_pitch_location(df, year): # データを pitch_type ごとにグループ分けする grouped = df.groupby('pitch_type') colors = {'FC': 'red', 'FF': 'blue', 'ST': 'green', 'KC': 'orange', 'CH': 'purple', 'SI': 'brown'} # pitch_type ごとに、'plate_x' を X 軸、'plate_z' を Y 軸とした散布図を作成する for pitch_type, data in grouped: plt.scatter(data['plate_x'], data['plate_z'], label=pitch_type, color=colors[pitch_type]) # ストライクゾーン x = [-0.88, 0.88, 0.88, -0.88, -0.88] y = [1.51, 1.51, 3.4, 3.4, 1.51] plt.fill(x, y, color='r', alpha=0.3) # 凡例を表示する plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left', borderaxespad=0) plt.xlim(-3, 4) plt.ylim(-3, 7) plt.xlabel('Plate X') plt.ylabel('Plate Z') # 罫線 plt.grid(which='both', linestyle='--', color='gray', alpha=0.5) plt.title(f'Pitch Location in {year}') # グラフを表示する plt.show() # 2019年、2020年、2021年のデータに対して投球位置をプロット plot_pitch_location(df_2019, 2019) plot_pitch_location(df_2020, 2020) plot_pitch_location(df_2021, 2021) 各年のリリースポイント (散布図)キャッチャーから見たリリース位置ですimport matplotlib.pyplot as plt def plot_release_point(df, year): # データを pitch_type ごとにグループ分けする grouped = df.groupby('pitch_type') colors = {'FC': 'red', 'FF': 'blue', 'ST': 'green', 'KC': 'orange', 'CH': 'purple', 'SI': 'brown'} # pitch_type ごとに、'release_pos_x' を X 軸、'release_pos_z' を Y 軸とした散布図を作成する for pitch_type, data in grouped: plt.scatter(data['release_pos_x'], data['release_pos_z'], label=pitch_type, color=colors[pitch_type]) # 凡例を表示する plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left', borderaxespad=0) plt.xlabel('Release Pos X') plt.ylabel('Release Pos Z') # 罫線 plt.grid(which='both', linestyle='--', color='gray', alpha=0.5) plt.title(f'Release Point in {year}') # X軸とY軸のレン.5ジを指定 plt.xlim(-2.5, -0.5) plt.ylim(4.5, 6.5) # グラフを表示する plt.show() # 2019年、2020年、2021年のデータに対してリリースポイントをプロット plot_release_point(df_2019, 2019) plot_release_point(df_2020, 2020) plot_release_point(df_2021, 2021) 各年の各球種ごとにリリースポイント (散布図)毎年少しづつ左に変わっていそう、フォームの微調整かimport matplotlib.pyplot as plt def plot_release_point_by_year(df_2019, df_2020, df_2021): # 3つのデータフレームを結合 combined_df = pd.concat([df_2019, df_2020, df_2021]) # データを pitch_type ごとにグループ分けする grouped = combined_df.groupby('pitch_type') # グラフの数と行列を指定 nrows = 3 ncols = 2 # サブプロットのタイトル用に球種を格納 titles = [] fig, axes = plt.subplots(nrows=nrows, ncols=ncols, figsize=(10, 15)) for idx, (pitch_type, data) in enumerate(grouped): # サブプロットのタイトルに球種を追加 titles.append(pitch_type) # サブプロットに対応する行と列のインデックスを計算 row = idx // ncols col = idx % ncols ax = axes[row][col] # 2019年、2020年、2021年のデータをそれぞれプロット data_2019 = data[data.index.isin(df_2019.index)] data_2020 = data[data.index.isin(df_2020.index)] data_2021 = data[data.index.isin(df_2021.index)] ax.scatter(data_2019['release_pos_x'], data_2019['release_pos_z'], label='2019', color='blue') ax.scatter(data_2020['release_pos_x'], data_2020['release_pos_z'], label='2020', color='red') ax.scatter(data_2021['release_pos_x'], data_2021['release_pos_z'], label='2021', color='green') ax.set_title(pitch_type) ax.set_xlabel('Release Pos X') ax.set_ylabel('Release Pos Z') # 罫線 ax.grid(which='both', linestyle='--', color='gray', alpha=0.5) # 凡例を表示する ax.legend() # グラフを表示する plt.tight_layout() plt.show() # 各球種ごとにグラフを分けて、2019年、2020年、2021年のデータを色分けしてプロット plot_release_point_by_year(df_2019, df_2020, df_2021) 各年の各球種ごとに投球コース (散布図)FC: カットファストボールは、ほぼ投げないコースがあるようだimport matplotlib.pyplot as plt def plot_pitch_location_by_year(df_2019, df_2020, df_2021): # 3つのデータフレームを結合 combined_df = pd.concat([df_2019, df_2020, df_2021]) # データを pitch_type ごとにグループ分けする grouped = combined_df.groupby('pitch_type') # グラフの数と行列を指定 nrows = 3 ncols = 2 # サブプロットのタイトル用に球種を格納 titles = [] fig, axes = plt.subplots(nrows=nrows, ncols=ncols, figsize=(10, 15)) for idx, (pitch_type, data) in enumerate(grouped): # サブプロットのタイトルに球種を追加 titles.append(pitch_type) # サブプロットに対応する行と列のインデックスを計算 row = idx // ncols col = idx % ncols ax = axes[row][col] # 2019年、2020年、2021年のデータをそれぞれプロット data_2019 = data[data.index.isin(df_2019.index)] data_2020 = data[data.index.isin(df_2020.index)] data_2021 = data[data.index.isin(df_2021.index)] ax.scatter(data_2019['plate_x'], data_2019['plate_z'], label='2019', color='blue') ax.scatter(data_2020['plate_x'], data_2020['plate_z'], label='2020', color='red') ax.scatter(data_2021['plate_x'], data_2021['plate_z'], label='2021', color='green') # ストライクゾーン x = [-0.88, 0.88, 0.88, -0.88, -0.88] y = [1.51, 1.51, 3.4, 3.4, 1.51] ax.fill(x, y, color='r', alpha=0.3) ax.set_title(pitch_type) ax.set_xlabel('Plate X') ax.set_ylabel('Plate Z') # 罫線 ax.grid(which='both', linestyle='--', color='gray', alpha=0.5) # 凡例を表示する ax.legend() # グラフを表示する plt.tight_layout() plt.show() # 各球種ごとにグラフを分けて、2019年、2020年、2021年のデータを色分けしてプロット plot_pitch_location_by_year(df_2019, df_2020, df_2021) 各年の各球種ごとにリリーススピンレート (Boxplot)2020からスピンレートが上がった球種がある 三振との関係などは次回import matplotlib.pyplot as plt import pandas as pd def plot_spin_rate_by_year_boxplot(df_2019, df_2020, df_2021, pitch_types): pitch_types = [pt for pt in pitch_types if pt is not None and pd.notna(pt)] # nan を除外 fig, axs = plt.subplots(len(pitch_types), 1, figsize=(8, len(pitch_types) * 4)) for i, pitch_type in enumerate(pitch_types): ax = axs[i] grouped_2019 = df_2019[df_2019['pitch_type'] == pitch_type].dropna(subset=['release_spin_rate']) grouped_2020 = df_2020[df_2020['pitch_type'] == pitch_type].dropna(subset=['release_spin_rate']) grouped_2021 = df_2021[df_2021['pitch_type'] == pitch_type].dropna(subset=['release_spin_rate']) data_to_plot = [] labels = [] if not grouped_2019.empty: data_to_plot.append(grouped_2019['release_spin_rate']) labels.append('2019') if not grouped_2020.empty: data_to_plot.append(grouped_2020['release_spin_rate']) labels.append('2020') if not grouped_2021.empty: data_to_plot.append(grouped_2021['release_spin_rate']) labels.append('2021') if data_to_plot: bp = ax.boxplot(data_to_plot, labels=labels) for j, d in enumerate(data_to_plot): mean_val = d.mean() ax.text(j + 0.8, mean_val, f"{mean_val:.1f}", ha='center', va='bottom', fontsize=10, color='blue') ax.set_title(f"Pitch Type: {pitch_type}") ax.set_xlabel('Year') ax.set_ylabel('Release Spin Rate') # 罫線 ax.grid(which='both', linestyle='--', color='gray', alpha=0.5) fig.suptitle('Release Spin Rate by Year and Pitch Type', fontsize=16, y=1.02) plt.tight_layout(pad=3) plt.show() # 2019年、2020年、2021年のデータに対してリリーススピンレートをプロット pitch_types_2019 = df_2019['pitch_type'].unique() pitch_types_2020 = df_2020['pitch_type'].unique() pitch_types_2021 = df_2021['pitch_type'].unique() # すべての年に存在する球種を取得 all_pitch_types = set(pitch_types_2019) | set(pitch_types_2020) | set(pitch_types_2021) plot_spin_rate_by_year_boxplot(df_2019, df_2020, df_2021, all_pitch_types) 各年の各球種ごとにスピード (Boxplot)スピードはおおきな変化なさそうimport matplotlib.pyplot as plt import pandas as pd def plot_release_speed_by_year_boxplot(df_2019, df_2020, df_2021, pitch_types): pitch_types = [pt for pt in pitch_types if pt is not None and pd.notna(pt)] # nan を除外 fig, axs = plt.subplots(len(pitch_types), 1, figsize=(8, len(pitch_types) * 4)) for i, pitch_type in enumerate(pitch_types): ax = axs[i] grouped_2019 = df_2019[df_2019['pitch_type'] == pitch_type].dropna(subset=['release_speed']) grouped_2020 = df_2020[df_2020['pitch_type'] == pitch_type].dropna(subset=['release_speed']) grouped_2021 = df_2021[df_2021['pitch_type'] == pitch_type].dropna(subset=['release_speed']) data_to_plot = [] labels = [] if not grouped_2019.empty: data_to_plot.append(grouped_2019['release_speed']) labels.append('2019') if not grouped_2020.empty: data_to_plot.append(grouped_2020['release_speed']) labels.append('2020') if not grouped_2021.empty: data_to_plot.append(grouped_2021['release_speed']) labels.append('2021') if data_to_plot: bp = ax.boxplot(data_to_plot, labels=labels) for j, d in enumerate(data_to_plot): mean_val = d.mean() ax.text(j + 0.8, mean_val, f"{mean_val:.1f}", ha='center', va='bottom', fontsize=10, color='blue') ax.set_title(f"Pitch Type: {pitch_type}") ax.set_xlabel('Year') ax.set_ylabel('Release Speed') # 罫線 ax.grid(which='both', linestyle='--', color='gray', alpha=0.5) fig.suptitle('Release Speed by Year and Pitch Type', fontsize=16, y=1.02) plt.tight_layout(pad=3) plt.show() # 2019年、2020年、2021年のデータに対してリリーススピードをプロット pitch_types_2019 = df_2019['pitch_type'].unique() pitch_types_2020 = df_2020['pitch_type'].unique() pitch_types_2021 = df_2021['pitch_type'].unique() # すべての年に存在する球種を取得 all_pitch_types = set(pitch_types_2019) | set(pitch_types_2020) | set(pitch_types_2021) plot_release_speed_by_year_boxplot(df_2019, df_2020, df_2021, all_pitch_types) release_extension: 投手のリリースポイントからホームプレートまでの距離（ft）各年の各球種ごとにリリースからホームまで (Boxplot)スピンレートとも多少は相関あるかもしれない。 距離が長くなるのは、投手側に不利になりそうだが。数字を読み違えてるかなあimport matplotlib.pyplot as plt import pandas as pd def plot_release_extension_by_year_boxplot(df_2019, df_2020, df_2021, pitch_types): pitch_types = [pt for pt in pitch_types if pt is not None and pd.notna(pt)] # nan を除外 fig, axs = plt.subplots(len(pitch_types), 1, figsize=(8, len(pitch_types) * 4)) for i, pitch_type in enumerate(pitch_types): ax = axs[i] grouped_2019 = df_2019[df_2019['pitch_type'] == pitch_type].dropna(subset=['release_extension']) grouped_2020 = df_2020[df_2020['pitch_type'] == pitch_type].dropna(subset=['release_extension']) grouped_2021 = df_2021[df_2021['pitch_type'] == pitch_type].dropna(subset=['release_extension']) data_to_plot = [] labels = [] if not grouped_2019.empty: data_to_plot.append(grouped_2019['release_extension']) labels.append('2019') if not grouped_2020.empty: data_to_plot.append(grouped_2020['release_extension']) labels.append('2020') if not grouped_2021.empty: data_to_plot.append(grouped_2021['release_extension']) labels.append('2021') if data_to_plot: bp = ax.boxplot(data_to_plot, labels=labels) for j, d in enumerate(data_to_plot): mean_val = d.mean() ax.text(j + 0.8, mean_val, f"{mean_val:.1f}", ha='center', va='bottom', fontsize=10, color='blue') ax.set_title(f"Pitch Type: {pitch_type}") ax.set_xlabel('Year') ax.set_ylabel('Release Extension') ax.grid(which='both', linestyle='--', color='gray', alpha=0.5) fig.suptitle('Release Extension by Year and Pitch Type', fontsize=16, y=1.02) plt.tight_layout(pad=3) plt.show() pitch_types_2019 = df_2019['pitch_type'].unique() pitch_types_2020 = df_2020['pitch_type'].unique() pitch_types_2021 = df_2021['pitch_type'].unique() all_pitch_types = set(pitch_types_2019) | set(pitch_types_2020) | set(pitch_types_2021) plot_release_extension_by_year_boxplot(df_2019, df_2020, df_2021, all_pitch_types) リリースからホームまでの距離 vs スピンレート (散布図)多少は相関あるのかもしれないが、見づらい。 何かそういう風にフォーム変えたのかなあimport matplotlib.pyplot as plt def plot_pitch_location_by_year(df_2019, df_2020, df_2021): combined_df = pd.concat([df_2019, df_2020, df_2021]) grouped = combined_df.groupby('pitch_type') nrows = 3 ncols = 2 titles = [] fig, axes = plt.subplots(nrows=nrows, ncols=ncols, figsize=(10, 15)) for idx, (pitch_type, data) in enumerate(grouped): titles.append(pitch_type) row = idx // ncols col = idx % ncols ax = axes[row][col] data_2019 = data[data.index.isin(df_2019.index)].dropna(subset=['release_extension', 'release_spin_rate']) data_2020 = data[data.index.isin(df_2020.index)].dropna(subset=['release_extension', 'release_spin_rate']) data_2021 = data[data.index.isin(df_2021.index)].dropna(subset=['release_extension', 'release_spin_rate']) ax.scatter(data_2019['release_extension'], data_2019['release_spin_rate'], label='2019', color='blue') ax.scatter(data_2020['release_extension'], data_2020['release_spin_rate'], label='2020', color='red') ax.scatter(data_2021['release_extension'], data_2021['release_spin_rate'], label='2021', color='green') ax.set_title(pitch_type) ax.set_xlabel('Release Extension') ax.set_ylabel('Release Spin Rate') ax.grid(which='both', linestyle='--', color='gray', alpha=0.5) ax.legend() plt.tight_layout() plt.show() plot_pitch_location_by_year(df_2019, df_2020, df_2021) リリースからホームまでの距離 vs スピンレート (Boxplot)無理やりboxplotで見たが、見づらい。def plot_spin_rate_vs_extension(df_2019, df_2020, df_2021, pitch_types): pitch_types = [ptype for ptype in pitch_types if not pd.isnull(ptype)] fig, axs = plt.subplots(len(pitch_types), 1, figsize=(12, len(pitch_types) * 4)) for i, pitch_type in enumerate(pitch_types): ax = axs[i] ax.set_title(f"Pitch Type: {pitch_type}") extension_bins = [4.8, 5.0, 5.2, 5.4, 5.6, 5.8, 6.0, 6.2, 6.4, 6.6] bin_labels = [f"{extension_bins[i]}-{extension_bins[i+1]}" for i in range(len(extension_bins)-1)] for year, df, color in zip([2019, 2020, 2021], [df_2019, df_2020, df_2021], ['C0', 'C1', 'C2']): data = df[df['pitch_type'] == pitch_type].dropna(subset=['release_spin_rate', 'release_extension']) if not data.empty: bins = pd.cut(data['release_extension'], extension_bins, labels=bin_labels) box_data = data.groupby(bins)['release_spin_rate'].apply(list) box_data = box_data[box_data.apply(lambda x: bool(x))] positions = [bin_labels.index(bin_label) + 1 + 0.2 * (year - 2020) for bin_label in box_data.index] bp = ax.boxplot(box_data.values, positions=positions, widths=0.15, patch_artist=True) for patch, col in zip(bp['boxes'], [color] * len(bp['boxes'])): patch.set(facecolor=col) for j, d in enumerate(box_data): mean_val = pd.Series(d).mean() if pd.notna(mean_val): ax.text(positions[j], mean_val, f"{mean_val:.1f}", ha='center', va='bottom', fontsize=10, color='blue') ax.set_xlabel('Release Extension') ax.set_ylabel('Release Spin Rate') # Change the y-axis label here ax.set_xticks(range(1, len(bin_labels) + 1)) ax.set_xticklabels(bin_labels) ax.grid(which='both', linestyle='--', color='gray', alpha=0.5) fig.suptitle('Release Spin Rate vs. Release Extension by Year and Pitch Type', fontsize=16, y=1.02) plt.tight_layout(pad=3) plt.show() plot_spin_rate_vs_extension(df_2019, df_2020, df_2021, all_pitch_types) 三振取った球とか、左右どちらに強いかとか、次は研究したい

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