def calcYearMCA(team, year):
teamEvents = gen.readTeamCsv("frc" + str(team), 'events', year)
yearMCA = 0
#Check if team actually had events in the current year, if not then zero mCA
if teamEvents is not None:
isOfficial = teamEvents['Type'].between(eventTypes.REGIONAL,
eventTypes.FOC,
inclusive=True)
officialEvents = teamEvents[isOfficial]
#Make sure the team actually had official events before trying any calc
if not officialEvents.empty:
teamYearAwards = gen.readTeamCsv('frc' + str(team), 'awards', year)
#If the team didn't win any awards then don't bother calculating per event mCA
if teamYearAwards is not None and not teamYearAwards.empty:
for event in officialEvents['Event']:
yearMCA += calcEventMCA(team, event)
#Previous year Elo values above the baseline increase mCA rating
if year == endYear:
teamPrevElo = df.loc[team, str(year) + ' Elo']
if teamPrevElo > eloBaseLine:
yearMCA += eloScale * (teamPrevElo - eloBaseLine)
return yearMCA
def getPerformanceData(team, year):
team = gen.teamString(team)
oprs = []
wins = 0
losses = 0
ties = 0
try:
matches = gen.readTeamCsv(team, 'matches', year)
if len(matches) > 0:
for idx, match in matches.iterrows():
result = gen.matchResult(team, match)
wins += 'WIN' == result
losses += 'LOSS' == result
ties += 'TIE' == result
except:
print("Could not retrieve matches for", team)
try:
events = gen.readTeamCsv(team, 'events', year)
if len(events) > 0:
for idx, e in events.iterrows():
try:
eOprs = gen.readEventCsv(e['Event'], 'opr')
teamOPR = eOprs[eOprs.Team == team]['OPR'].values[0]
oprs.append(teamOPR)
except Exception as e:
print(e)
except:
print("Could not retrieve events for", team)
if len(oprs) is 0:
maxOPR = 0
avgOPR = 0
else:
maxOPR = max(oprs)
avgOPR = stat.mean(oprs)
played = wins + losses + ties
winPercent = 0
if played > 0:
winPercent = wins / played
teamKey = gen.teamNumber(team)
return {
'Team': teamKey,
'Max OPR': maxOPR,
'Avg OPR': avgOPR,
'Wins': wins,
'Losses': losses,
'Ties': ties,
'Win %': winPercent
}
def getData(year, team):
playPoints = 0
awardPoints = 0
teamEvents = gen.readTeamCsv(team, 'events', year)
if teamEvents is not None:
official = teamEvents[teamEvents.Type < 10]
if len(official) > 0:
firstEvent = official.iloc[0]['Event']
playPoints, awardPoints = teamAtEvent(team, firstEvent)
return {'team': team, 'playPoints': playPoints, 'awardPoints': awardPoints}
def getTeamRatingData(team, yearDepth=3, YEAR=None):
overallRating = 0
playRating = 0
teamTotal = 0
eventMax = 0
eventCount = 0
eventAvg = 0
team = gen.teamString(team)
teamYears = getTeamYears(team, YEAR)
pastYears = sorted(teamYears[-yearDepth:], key=int, reverse=True)
tmpYears = pastYears[:]
for year in tmpYears:
if year < YEAR - yearDepth:
pastYears.remove(year)
for count, year in enumerate(pastYears):
yearPoints = 0
yearPlayPoints = 0
for event in gen.readTeamCsv(team, 'events', year)['Event']:
eventPlayPoints, eventAwardPoints = getTeamEventPoints(team, event)
eventPoints = eventPlayPoints + eventAwardPoints
yearPoints += eventPoints
yearPlayPoints += eventPlayPoints
if eventPoints > 0:
eventCount += 1
eventMax = eventPoints if eventPoints > eventMax else eventMax
playRating += yearPlayPoints / pow(2, count * 2)
overallRating += yearPoints / pow(2, count * 2)
teamTotal += yearPoints
eventAvg = 0
if eventCount > 0:
eventAvg = teamTotal / eventCount
teamKey = gen.teamNumber(team)
return {
'Team': teamKey,
'Overall Rating': overallRating,
'Event Max': eventMax,
'Total Points': teamTotal,
'Year Avg': teamTotal / yearDepth,
'Event Avg': eventAvg,
'Events': eventCount,
'Play Rating': playRating
}
def saveTeamYearMatches(year, team):
fileExists, fullPath = filePathHandler('teams', team, 'matches', year)
fileExists = False
if not fileExists:
try:
teamEvents = gen.readTeamCsv(team, 'events', year)
teamMatches = pd.concat(
gen.teamEventMatches(team, event)
for event in teamEvents['Event'])
teamMatches.to_csv(fullPath + '.csv', index=False)
except Exception as e:
print(e)
def saveTeamAwards(year, team):
fileExists, fullPath = filePathHandler('teams', team, 'awards', year)
eventData = gen.readTeamCsv(team, 'events', year)
fileExists = False
fileExists = False
if not fileExists:
try:
teamYearAwards = []
for event in eventData['Event']:
awardsExist, awardsPath = filePathHandler(
'events', event, 'awards')
if awardsExist:
evAwards = pd.read_csv(awardsPath + '.csv',
index_col=False,
names=['Award', 'Name', 'Team'])
evAwards.Team = evAwards.Team.str.strip()
if type(evAwards['Team'][0]) is not str:
evAwards['Team'] = 'frc' + evAwards['Team'].astype(str)
teamAwards = evAwards[evAwards.Team == team]
for idx, row in teamAwards.iterrows():
awardType = row['Award'].split('_')[1]
awardName = row['Name']
teamYearAwards.append({
'Event': event,
'Type': awardType,
'Name': awardName
})
colOrder = ['Event', 'Type', 'Name']
gen.listOfDictToCSV(fullPath, teamYearAwards, colOrder, False)
except Exception as e:
print(e)
def calcEventMCA(team, event):
eventMCA = 0
teamAwards = gen.readTeamCsv('frc' + str(team), 'awards', int(event[:4]))
#Don't bother doing calc if the team didn't win awards that year
#This check is duplicated in calcYearMCA, but included here as well in case
#function is run independently.
if teamAwards is not None:
eventFilter = teamAwards['Event'] == event
currentAwards = teamAwards[eventFilter]
#If the team didn't win any awards at this event don't calc.
if not currentAwards.empty:
eventValuation = len(gen.readEventCsv(event,
'teams')) / baseTeamCount
for idx, award in currentAwards.iterrows():
if award['Type'] in awardValues:
#For CA Finalist or Honorable Mention treat it as 100 team event
if award['Type'] == awardTypes.CHAIRMANS_FINALIST or award[
'Type'] == awardTypes.CHAIRMANS_HONORABLE_MENTION:
eventValuation = 2
eventMCA += eventValuation * awardValues[award['Type']]
return eventMCA
for year in years:
yr = str(year)
dist = yr + district
yearData[yr] = {}
yearData[yr]['Teams'] = tba.district_teams(dist, False, True)
yearData[yr]['Points'] = {
t['team_key']: t['point_total']
for t in tba.district_rankings(dist)
}
currentTeams = tba.district_teams(str(currentYear) + district, False, True)
teamData = []
for team in currentTeams:
events = gen.readTeamCsv(team, 'events', currentYear - 1)
tm = tba.team(team)
maxOPR = 0
totalOPR = 0
avgOPR = 0
eventAdjust = 0
if events is not None:
seasonEvents = events[events.Type < 4]['Event']
for event in seasonEvents:
eventData = gen.readEventCsv(event, 'opr')
teamRow = eventData[eventData['Team'] == team]
if teamRow.empty:
eventAdjust += 1
teamList[year] = gen.readTeamListCsv(year)['Teams'].tolist()
eventList[year] = slff.fetchNormalEventData(year)
#teams = gen.readTeamListCsv(2019)['Teams'].tolist()
teams = tba.district_teams('2019tx', False, True)
#teams = ['frc125', 'frc401', 'frc7179']
eventFields = ['Code', 'Type', 'Week', 'Total Points', 'Award Points', 'Award Names', 'Draft Points', 'Rank Points', 'Elim Points', 'Rank']
teamData = {}
for team in tqdm(teams):
teamData[team] = {}
for year in yearRange:
teamData[team][year] = []
if team in teamList[year]:
teamEvents = gen.readTeamCsv(team, 'events', year)
for idx, event in teamEvents.iterrows():
eventKey = event['Event']
eventType = 'District' if event['Type'] == 1 else 'Regional'
if eventKey in eventList[year]:
awardData = slff.getAwardPoints(team, eventKey)
playData = slff.getPlayPoints(team, eventKey)
totalPoints = sum(playData[:3]) + awardData[0]
eventDict = [eventKey, eventType, eventList[year][eventKey], totalPoints] + awardData + playData
eventPacket = {}
for idx, field in enumerate(eventFields):
eventPacket[field] = eventDict[idx]
teamData[team][year].append(eventPacket)
def prepFields():
fields = ['Team']
def getAwardPoints(team, event, eventType, useTba=False):
ROBOT_AWARDS = [16, 17, 20, 21, 29, 71]
OTHER_AWARDS = [5, 11, 13, 14, 18, 22, 27, 30]
NO_POINTS = [1, 2]
CA = 0
EI = 9
WF = 3
DEANS = 4
RAS = 10
RI = 15
CAF = 69
WILDCARD = 68
awardData = {
'REG': {
CA: 60,
EI: 45,
RAS: 25,
RI: 15,
WF: 10,
DEANS: 5,
WILDCARD: 0,
'ROBOT': 20,
'OTHER': 5,
'NONE': 0
},
'CMP': {
CA: 110,
EI: 60,
RAS: 35,
RI: 20,
WF: 30,
DEANS: 15,
CAF: 90,
'ROBOT': 30,
'OTHER': 10,
'NONE': 0
}
}
awardPoints = 0
if useTba:
try:
tya = tba.team_awards(team, None, event)
for award in tya:
awardType = award['award_type']
if awardType in ROBOT_AWARDS:
awardType = 'ROBOT'
if awardType in OTHER_AWARDS:
awardType = 'OTHER'
if awardType in NO_POINTS:
awardType = 'NONE'
awardPoints += awardData[eventType][awardType]
except:
pass
else:
tya = gen.readTeamCsv(team, 'awards', event[:4])
if tya is not None:
teamEventAwards = tya[tya.Event == event]
for idx, row in teamEventAwards.iterrows():
awardType = row['Type']
if awardType in ROBOT_AWARDS:
awardType = 'ROBOT'
if awardType in OTHER_AWARDS:
awardType = 'OTHER'
if awardType in NO_POINTS:
awardType = 'NONE'
if awardType is CA and eventType == 'CMP':
awardPoints -= 90
awardPoints += awardData[eventType][awardType]
return awardPoints
import gen
tba = gen.setup()
dist = 'ont'
startYear = 2014
currYear = 2018
distTeams = tba.district_teams(str(currYear) + dist, False, True)
records = []
for team in distTeams:
wins = 0
for year in range(startYear, currYear + 1):
awards = gen.readTeamCsv(team, 'awards', year)
if awards is not None:
for idx, award in awards.iterrows():
if award['Type'] == 1:
wins += 1
records.append({'Team': team[3:], 'Wins': wins})
records = sorted(records, key=lambda k: k['Wins'], reverse=True)
gen.listOfDictToCSV(
dist.upper() + " " + str(startYear) + " to " + str(currYear) + " Wins",
records, ['Team', 'Wins'])
import gen
teams = gen.readTeamListCsv(2018)['Teams']
records = []
for team in teams:
matches = gen.readTeamCsv(team, 'matches', 2018)
wins = 0
losses = 0
ties = 0
if matches is not None:
for idx, match in matches.iterrows():
result = gen.matchResult(team, match)
wins += result == 'WIN'
losses += result == 'LOSS'
ties += result == 'TIE'
total = wins + losses + ties
if total != 0:
if wins == 0:
wins = 1
total = total * 10
rating = wins / total
wins = 0
else:
rating = wins / total
recordString = str(wins) + ' / ' + str(losses) + ' / ' + str(ties)
records.append({
import slff
currentYear = 2018
rookieYear = 2006
prevTeams = gen.readTeamListCsv(rookieYear - 1)['Teams']
currTeams = gen.readTeamListCsv(rookieYear)['Teams']
rookies = []
for team in currTeams:
prevYears = slff.getTeamYears(team, rookieYear - 1)
if len(prevYears) == 0:
print(team)
#Event data (ranks, opr)
events = gen.readTeamCsv(team, 'events', currentYear)
ranks = []
oprs = []
avgOpr = 0
avgRank = 99
if events is not None:
for event in events['Event']:
rank = gen.readEventCsv(event, 'rankings')
opr = gen.readEventCsv(event, 'opr')
if opr is not None:
oprs.append(opr[opr.Team == team]['OPR'].values[0])
else:
print("Couldn't get OPR data for", event)
if rank is not None:
if len(rank.columns) > 2:
def teamAtEvent(team, event):
ROBOT_AWARDS = [16, 17, 20, 21, 29, 71]
OTHER_AWARDS = [5, 11, 13, 14, 18, 22, 27, 30]
NO_POINTS = [1, 2]
CA = 0
EI = 9
WF = 3
DEANS = 4
RAS = 10
RI = 15
CAF = 69
WILDCARD = 68
awardData = {
'REG': {
CA: 25,
EI: 15,
RAS: 5,
RI: 5,
WF: 5,
DEANS: 5,
WILDCARD: 0,
'ROBOT': 10,
'OTHER': 5,
'NONE': 0
},
'CMP': {
CA: 110,
EI: 60,
RAS: 35,
RI: 20,
WF: 30,
DEANS: 15,
CAF: 90,
'ROBOT': 30,
'OTHER': 10,
'NONE': 0
}
}
awardPoints = 0
playPoints = 0
#Fetch event info, do this first since we only want to really process official events.
eventInfo = gen.readEventCsv(event, 'info')
if eventInfo is not None:
isOfficial = eventInfo['Type'][0] in range(0, 7)
isChamps = eventInfo['Type'][0] in range(3, 5)
eventType = 'CMP' if isChamps else 'REG'
if isOfficial:
playPoints = calcPoints(team, event)
tya = gen.readTeamCsv(team, 'awards', event[:4])
if tya is not None:
teamEventAwards = tya[tya.Event == event]
for idx, row in teamEventAwards.iterrows():
awardType = row['Type']
if awardType in ROBOT_AWARDS:
awardType = 'ROBOT'
if awardType in OTHER_AWARDS:
awardType = 'OTHER'
if awardType in NO_POINTS:
awardType = 'NONE'
awardPoints += awardData[eventType][awardType]
return [playPoints, awardPoints]
def getAwardPoints(team, event):
ROBOT_AWARDS = [16, 17, 20, 21, 29, 71]
OTHER_AWARDS = [11, 13, 18, 22, 27, 30]
NO_POINTS = [1, 2, 5, 14]
CA = 0
EI = 9
WF = 3
DEANS = 4
RAS = 10
RI = 15
WILDCARD = 68
numberToName = {
0: 'CA',
1: 'Winner',
2: 'Finalist',
9: 'EI',
3: 'WF',
4: 'DEANS',
10: 'RAS',
15: 'RI',
16: 'ID',
17: 'Quality',
20: 'Creativity',
21: 'EE',
29: 'Control',
71: 'Auto',
11: 'GP',
13: 'Judges',
18: 'Safety',
22: 'Ent',
27: 'Imagery',
30: 'Spirit'
}
awardValues = {
CA: 60,
EI: 45,
RAS: 25,
RI: 15,
WF: 10,
DEANS: 5,
WILDCARD: 0,
'ROBOT': 20,
'OTHER': 5,
'NONE': 0
}
awardPoints = 0
awardNames = []
tya = gen.readTeamCsv(team, 'awards', event[:4], ['Event', 'Type', 'Name'])
if tya is not None:
teamEventAwards = tya[tya.Event == event]
for idx, row in teamEventAwards.iterrows():
awardType = row['Type']
if awardType in ROBOT_AWARDS:
awardType = 'ROBOT'
if awardType in OTHER_AWARDS:
awardType = 'OTHER'
if awardType in NO_POINTS:
awardType = 'NONE'
if row['Type'] in numberToName.keys():
awardNames.append(numberToName[row['Type']])
awardPoints += awardValues[awardType]
return [awardPoints, ' / '.join(awardNames)]
yearStart = 2016
yearEnd = 2018
teamList = {}
eventList = {}
for year in range(yearStart, 2020):
teamList[year] = gen.readTeamListCsv(year)['Teams'].tolist()
eventList[year] = slff.fetchNormalEventData(year)
maxEvents = 0
maxTeam = ''
maxYear = 0
maxEventCodes = []
for team in tqdm(teamList[2019]):
for year in range(yearStart, 2019):
teamEvents = 0
eventCodes = []
eventDF = gen.readTeamCsv(team, 'events', year)
if eventDF is not None:
for event in eventDF['Event']:
if event in eventList[year]:
eventCodes.append(event)
teamEvents += 1
if teamEvents > maxEvents:
maxEventCodes = eventCodes
maxEvents = teamEvents
maxTeam = team
maxYear = year
print(maxEvents, maxTeam, year)
print(maxEventCodes)
