def get_optimal_lineups(year, season_gp):
optimal_query = """SELECT team_abb,
starter_val, bullpen_val,
l.lineup_val AS lineup_vsL, r.lineup_val AS lineup_vsR,
total_val + 0.25*(l.lineup_val) + 0.75*(r.lineup_val) AS roster_WAR,
starter_var, bullpen_var,
l.lineup_var AS vsL_var, r.lineup_var AS vsR_var,
total_var + 0.25*l.lineup_var + 0.75*r.lineup_var AS roster_var
FROM __optimal_pitching p
JOIN __optimal_lineups l USING (team_abb)
JOIN __optimal_lineups r USING (team_abb)
WHERE l.vs_hand = 'l'
AND r.vs_hand = 'r'
AND l.dh_name IS NOT NULL
AND r.dh_name IS NOT NULL
ORDER BY team_abb ASC;"""
total_roster_war_query = """SELECT
SUM(p.total_val + 0.25*(l.lineup_val) + 0.75*(r.lineup_val)) AS roster_WAR
FROM __optimal_pitching p
JOIN __optimal_lineups l USING (team_abb)
JOIN __optimal_lineups r USING (team_abb)
WHERE l.vs_hand = 'l'
AND r.vs_hand = 'r'
AND l.dh_name IS NOT NULL
AND r.dh_name IS NOT NULL;"""
# should be around ~1000
total_roster_war = db.query(total_roster_war_query)[0][0]
# should be around 48 (48-114 replacement level?)
replacement_team_wins = (2430-float(total_roster_war))/30
# should be around .300
rep_team_win_pct = float(replacement_team_wins)/162
optimal_res = db.query(optimal_query)
for row in optimal_res:
entry = {}
team_abb, starter_val, bullpen_val, lu_vsL, lu_vsR, roster_WAR, starter_var, bullpen_var, vsL_var, vsR_var, roster_var = row
mascot_name = helper.get_mascot_names(team_abb.upper(), year)
team_name, games_played, rep_WAR, oWAR, dWAR, FIP_WAR, W, L, py_W, py_L = get_standing_metrics(year, mascot_name)
team_abb = helper.get_team_abb(team_name, year)
# mascot_name = helper.get_mascot_names(team_abb.upper(), year-1)
# team_name, games_played, rep_WAR, oWAR, dWAR, FIP_WAR, W, L, py_W, py_L = get_standing_metrics(year, mascot_name)
# team_abb = helper.get_team_abb(team_name, year-1)
games_played = float(games_played)
if games_played > 162.0:
roster_W = float(roster_WAR) + rep_team_win_pct*games_played
roster_pct = roster_W/games_played
ros_g = 0
else:
roster_W = float(roster_WAR) + rep_team_win_pct*162
roster_pct = roster_W/162.0
ros_g = 162-games_played
try:
w_pct = float(W)/float(W+L)
py_pct = float(py_W)/float(py_W+py_L)
except ZeroDivisionError:
w_pct = 0.5
py_pct = 0.5
# logistic weights for pythag% and win&
# rest of season projected win% = (1-2w)*(roster%) + w(pythag%) + w(win%)
# where w = (0.25) / (1+20e^(-0.035*games_played))
if games_played <= 10:
current_weight = 0.0015*float(games_played)
else:
current_weight = 0.25 / (1 + 20*math.exp(-0.035*float(games_played)))
ros_pct = (1-2*current_weight)*roster_pct + (current_weight)*max(py_pct, 0.25) + (current_weight)*max(w_pct, 0.25)
ros_W = ros_pct*ros_g
# for the total amount of variance for the team, we first take the total amount of variance from team projections (based on the variance in each individual player's projection)
total_roster_var = float(roster_var)
# then we add a measure of variance based on the difference between true talent record (pythag record) and observed record (see /variance_research/Full Season Pythag Standings std research.png)
total_roster_var += -0.0055021865*(ros_pct*162) + 3.4706743014
# Finally we add a value of 5.0 to the STANDARD DEVIATION (not variance). We can express the amount of variance desired to add in the set of equations {std = sqrt(v), std+5.0 = sqrt(v+c)}, and then solving for c (https://tinyurl.com/y8tk64ez)
# NB. the value of 5.0 is a guess (~0.33 win for each starter plus a small amount for bench players and relief pitchers) and hack-y and should be cleaned up, or at least weighted more towards defensive #s over wOBA numbers) wins to the variance due to my uncertain nature (mostly from defense) of my conversion from raw ZiPS to DMB WAR (i.e., I think if my projection says the team is a true talent 90 win team, I think there is +/- 5.0 wins of standard deviation in that projection)
total_roster_var += 10*math.sqrt(total_roster_var) + 25
projected_W = W + ros_W
if games_played > 162.0:
roster_L = games_played - roster_W
projected_L = games_played - projected_W
projected_pct = projected_W/games_played
else:
roster_L = 162.0 - roster_W
projected_L = 162.0 - projected_W
projected_pct = projected_W/162.0
entry['team_abb'] = team_abb
entry['team_name'] = team_name
entry['year'] = year
entry['season_gp'] = season_gp
entry['games_played'] = games_played
entry['starter_val'] = starter_val
entry['bullpen_val'] = bullpen_val
entry['vsR_val'] = lu_vsR
entry['vsL_val'] = lu_vsL
entry['roster_strength'] = roster_WAR
entry['starter_var'] = starter_var
entry['bullpen_var'] = bullpen_var
entry['vsR_var'] = vsR_var
entry['vsL_var'] = vsL_var
entry['roster_var'] = roster_var
entry['overall_var'] = total_roster_var
entry['roster_W'] = roster_W
entry['roster_L'] = roster_L
entry['roster_pct'] = roster_pct
entry['current_W'] = W
entry['current_L'] = L
entry['current_pct'] = w_pct
entry['ros_W'] = ros_W
entry['ros_L'] = ros_g - ros_W
entry['ros_pct'] = ros_pct
entry['projected_W'] = projected_W
entry['projected_L'] = projected_L
entry['projected_pct'] = projected_pct
# raw_input(entry)
db.insertRowDict(entry, '__team_strength', insertMany=False, replace=True, rid=0,debug=1)
db.conn.commit()
def process():
print "processed_team_standings_advanced"
table = 'processed_team_standings_advanced'
db.query("TRUNCATE TABLE `" + table + "`")
entries = []
teamWAR_qry = """SELECT
year,
team_abb,
dWAR,
oWAR,
(replacement/10) as repWAR,
FIP_WAR,
ERA_WAR
FROM processed_WAR_team
"""
team_WAR_list = db.query(teamWAR_qry)
for team in team_WAR_list:
year, team_abb, dWAR, oWAR, repWAR, FIP_WAR, ERA_WAR = team
mascot_name = helper.get_mascot_names(team_abb.upper(), year)
#a full season is ~17 replacement wins?
repWAR = float(repWAR)
pos_WAR = float(dWAR) + float(oWAR) + repWAR
fWAR = pos_WAR + float(FIP_WAR)
rWAR = pos_WAR + float(ERA_WAR)
if team_abb == '':
continue
else:
record_q = """SELECT
year,
team_name,
games_played,
w,
l,
rf,
ra
FROM team_standings
WHERE team_name LIKE '%%%s%%'
AND year = %s
AND games_played = (SELECT MAX(games_played) FROM team_standings WHERE team_name LIKE '%%%s%%' AND year = %s)
"""
record_qry = record_q % (mascot_name, year, mascot_name, year)
# raw_input(record_qry)
record = db.query(record_qry)[0]
year, team_name, games_played, w, l, rf, ra = record
# http://www.had2know.com/sports/pythagorean-expectation-win-percentage-baseball.html
pythag_x = ((float(rf) + float(ra)) /
(float(w) + float(l)))**(float(0.285))
pythag_win_pct = (float(rf)**pythag_x) / ((float(rf)**pythag_x) +
(float(ra)**pythag_x))
pythag_wins = (w + l) * pythag_win_pct
pythag_losses = games_played - (pythag_wins)
if year < 2017:
rep_team_win_pct = 0.300
else:
rep_team_win_pct = 0.325
rep_team_wins = rep_team_win_pct * games_played
# f_wins = (pos_WAR/repWAR)*17.0 + float(FIP_WAR) + rep_team_wins
# f_losses = games_played - (f_wins)
# r_wins = (pos_WAR/repWAR)*17.0 + float(ERA_WAR) + rep_team_wins
# r_losses = games_played - (r_wins)
f_wins = fWAR + rep_team_wins
f_losses = games_played - (f_wins)
r_wins = rWAR + rep_team_wins
r_losses = games_played - (r_wins)
entry = {
"year": year,
"team_name": team_name,
"games_played": games_played,
"repWAR": repWAR,
"oWAR": oWAR,
"dWAR": dWAR,
"FIP_WAR": FIP_WAR,
"ERA_WAR": ERA_WAR,
"RF": rf,
"RA": ra,
"f_Wins": f_wins,
"f_Losses": f_losses,
"r_Wins": r_wins,
"r_Losses": r_losses,
"py_Wins": pythag_wins,
"py_Losses": pythag_losses,
"W": w,
"L": l
}
entries.append(entry)
if entries != []:
db.insertRowDict(entries, table, replace=True, insertMany=True, rid=0)
db.conn.commit()