def combine_player_info(player_id, dbsession=DBSESSION):
"""
Get player's name, club, recent scores, upcoming fixtures, and
upcoming predictions if available
"""
info_dict = {"player_id": player_id}
p = get_player(player_id, dbsession=dbsession)
info_dict["player_name"] = p.name
team = p.team(CURRENT_SEASON, NEXT_GAMEWEEK)
info_dict["team"] = team
# get recent scores for the player
rs = get_recent_scores_for_player(p, dbsession=dbsession)
recent_scores = [{"gameweek": k, "score": v} for k, v in rs.items()]
info_dict["recent_scores"] = recent_scores
# get upcoming fixtures
fixtures = get_fixtures_for_player(p, dbsession=dbsession)[:3]
info_dict["fixtures"] = []
for f in fixtures:
home_or_away = "home" if f.home_team == team else "away"
opponent = f.away_team if home_or_away == "home" else f.home_team
info_dict["fixtures"].append({
"gameweek": f.gameweek,
"opponent": opponent,
"home_or_away": home_or_away
})
try:
tag = get_latest_prediction_tag(dbsession=dbsession)
predicted_points = get_predicted_points_for_player(p,
tag,
dbsession=dbsession)
info_dict["predictions"] = predicted_points
except (RuntimeError):
pass
return info_dict
def calc_predicted_points_for_player(
player,
team_model,
df_player,
df_bonus,
df_saves,
df_cards,
season,
gw_range=None,
fixtures_behind=3,
tag="",
dbsession=session,
):
"""
Use the team-level model to get the probs of scoring or conceding
N goals, and player-level model to get the chance of player scoring
or assisting given that their team scores.
"""
message = "Points prediction for player {}".format(player.name)
if not gw_range:
# by default, go for next three matches
gw_range = list(
range(NEXT_GAMEWEEK, min(NEXT_GAMEWEEK + 3, 38))
) # don't go beyond gw 38!
team = player.team(
season, gw_range[0]
) # assume player stays with same team from first gameweek in range
position = player.position(season)
fixtures = get_fixtures_for_player(
player, season, gw_range=gw_range, dbsession=dbsession
)
# use same recent_minutes from previous gameweeks for all predictions
recent_minutes = get_recent_minutes_for_player(
player,
num_match_to_use=fixtures_behind,
season=season,
last_gw=min(gw_range) - 1,
dbsession=dbsession,
)
if len(recent_minutes) == 0:
# e.g. for gameweek 1
# this should now be dealt with in get_recent_minutes_for_player, so
# throw error if not.
# recent_minutes = estimate_minutes_from_prev_season(
# player, season=season, dbsession=session
# )
raise ValueError("Recent minutes is empty.")
expected_points = defaultdict(float) # default value is 0.
predictions = [] # list that will hold PlayerPrediction objects
for fixture in fixtures:
gameweek = fixture.gameweek
is_home = fixture.home_team == team
opponent = fixture.away_team if is_home else fixture.home_team
home_or_away = "at home" if is_home else "away"
message += "\ngameweek: {} vs {} {}".format(gameweek, opponent, home_or_away)
points = 0.0
expected_points[gameweek] = points
if sum(recent_minutes) == 0:
# 'recent_minutes' contains the number of minutes that player played
# for in the past few matches. If these are all zero, we will for sure
# predict zero points for this player, so we don't need to call all the
# functions to calculate appearance points, defending points, attacking points.
points = 0.0
elif is_injured_or_suspended(player.fpl_api_id, gameweek, season, dbsession):
# Points for fixture will be zero if suspended or injured
points = 0.0
else:
# now loop over recent minutes and average
points = 0
for mins in recent_minutes:
points += (
get_appearance_points(mins)
+ get_attacking_points(
player.player_id,
position,
team,
opponent,
is_home,
mins,
team_model,
df_player,
)
+ get_defending_points(
position, team, opponent, is_home, mins, team_model
)
)
if df_bonus is not None:
points += get_bonus_points(player.player_id, mins, df_bonus)
if df_cards is not None:
points += get_card_points(player.player_id, mins, df_cards)
if df_saves is not None:
points += get_save_points(
position, player.player_id, mins, df_saves
)
points = points / len(recent_minutes)
# create the PlayerPrediction for this player+fixture
predictions.append(make_prediction(player, fixture, points, tag))
expected_points[gameweek] += points
# and return the per-gameweek predictions as a dict
message += "\nExpected points: {:.2f}".format(points)
print(message)
return predictions
