for idx, team in enumerate(draftData[drafter]):
earliestEvent = detroit
for event in tba.team_events(team, year):
if event['start_date'] < earliestEvent['start_date']:
if event['event_type'] in [
eventTypes.DISTRICT, eventTypes.REGIONAL
]:
earliestEvent = event
playPoints = sum(
slffFunctions.getPlayPoints('frc' + str(team),
earliestEvent['key'])[0:3])
awardPoints = slffFunctions.getAwardPoints(team,
earliestEvent['key'])[0]
teamPoints = playPoints + awardPoints
drafterData['P' + str(idx + 1) + ' Team'] = team
drafterData['P' + str(idx + 1) + ' Score'] = teamPoints
drafterData['Total'] += teamPoints
drafterScores.append(drafterData)
drafterScores = sorted(drafterScores, key=lambda k: k['Total'], reverse=True)
cols = [
'Drafter', 'Total', 'P1 Team', 'P1 Score', 'P2 Team', 'P2 Score',
'P3 Team', 'P3 Score'
]
gen.listOfDictToCSV(
str(year) + ' World Wide Regional Scores', drafterScores, cols)
if team in blueTeams:
slot = blueTeams.index(team) + 1
color = 'blue'
else:
slot = redTeams.index(team) + 1
color = 'red'
climbs.append(
habLevels.index(
match['score_breakdown'][color]['endgameRobot' +
str(slot)]))
starts.append(
habLevels.index(
match['score_breakdown'][color]['preMatchLevelRobot' +
str(slot)]))
lineCross.append(match['score_breakdown'][color][
'habLineRobot' + str(slot)] == 'CrossedHabLineInSandstorm')
except:
print('Match', match['key'], 'no work')
outList.append({
'Team': team,
'Avg Climb': stat.mean(climbs),
'Avg Start': stat.mean(starts),
'Max Start': max(starts),
'Line Cross': sum(lineCross) / matches
})
gen.listOfDictToCSV(
'2019chcmpData', outList,
['Team', 'Avg Climb', 'Avg Start', 'Max Start', 'Line Cross'])
import gen
import eventTypes
tba = gen.setup()
year = 2019
regionals = []
for event in tba.events(2019):
if event['event_type'] == eventTypes.REGIONAL:
if event['country'] != 'China':
regionals.append({
'Code': event['key'],
'Week': event['week'],
'Start Date': event['start_date']
})
regionals = sorted(regionals, key=lambda k: k['Start Date'])
gen.listOfDictToCSV(
str(year) + ' Regionals', regionals, ['Code', 'Week', 'Start Date'])
import gen
tba = gen.setup()
eventList = tba.events(2018)
data = []
for event in eventList:
if event['event_type'] in range(0, 10):
data.append({
'Name': event['name'],
'State': event['state_prov'],
'Country': event['country'],
'Type': event['event_type_string']
})
gen.listOfDictToCSV('2018 Event Base Data', data,
['Name', 'State', 'Country', 'Type'])
import eventTypes
import datetime
import gen
current_date = datetime.datetime.now().strftime('%Y-%m-%d')
tba = gen.setup()
offList = []
for event in tba.events(2019):
if event.event_type == eventTypes.OFFSEASON:
if event.start_date > current_date:
offList.append({
'name': event.name,
'code': event.key,
'start': event.start_date,
'end': event.end_date
})
offList = sorted(offList, key=lambda k: k['start'])
gen.listOfDictToCSV('2019 OFF Events', offList,
['name', 'code', 'start', 'end'])
if elims['current_level_record']['wins'] > 0:
elimPoints += 4
elif elims['level'] == 'sf':
elimPoints = 10
if elims['current_level_record']['wins'] > 0:
elimPoints += 2
elif elims['level'] == 'qf':
elimPoints = 4
if elims['current_level_record']['wins'] > 0:
elimPoints += 1
inMatch = 0
for match in elimMatches:
inMatch += team in match['alliances']['blue']['team_keys']
inMatch += team in match['alliances']['red']['team_keys']
rec = status['playoff']['record']
backup = inMatch / (rec['wins'] + rec['losses'] + rec['ties']) + 0.5
scales = [1.05, 1.0, 0.95, backup]
position = status['alliance']['pick']
scaleFactor = max([scales[position], .25])
total = (elimPoints + qualPoints + rankPoints) * scaleFactor
data.append({'team': team[3:], 'rank': rank, 'quals': qualPoints, 'rankP': rankPoints, 'elims': elimPoints, 'total': total, 'factor': scales[position]})
colOrder = ['team', 'rank', 'total', 'factor', 'rankP', 'quals', 'elims']
gen.listOfDictToCSV(EVENT+'FFIT', data, colOrder)
result = {
'Username': userName,
'Forum Count': forumCount,
'Forums': forums,
'Post Count': postCount,
'Team Number': teamNumber,
'Rookie Year': rookieYear,
'Last Activity': lastActive
}
return result
now = datetime.datetime.now()
timeStamp = now.strftime("%Y-%m-%d")
r = requests.get(modUrl)
soup = BeautifulSoup(r.content, "html.parser")
modTable = soup.find_all('table')[7].find_all('tr')
modData = []
for row in tqdm(modTable):
result = processMod(row)
if result is not None:
modData.append(result)
gen.listOfDictToCSV('cdMods-' + timeStamp, modData, [
'Username', 'Forum Count', 'Post Count', 'Team Number', 'Rookie Year',
'Last Activity', 'Forums'
])
import gen
tba = gen.setup()
dist = 'chs'
year = 2019
distKey = str(year) + dist
distEvents = tba.district_events(distKey, False, True)
oodPlays = []
for team in tba.district_teams(distKey, False, True):
print("Processing team " + team)
for event in tba.team_events(team, year, False):
if event['key'] not in distEvents:
teamModel = {
'Team': team,
'Event': event['event_code'],
'Type': event['event_type']
}
oodPlays.append(teamModel)
gen.listOfDictToCSV(distKey + "_ood", oodPlays, ['Team', 'Event', 'Type'])
eventData['Quals Matches'] += 1
else:
eventData['Elims Matches'] += 1
if match['alliances']['red']['score'] < totalPts or match[
'alliances']['blue']['score'] < totalPts:
if match['comp_level'] == 'qm':
eventData['Quals Wins'] += 1
else:
eventData['Elims Wins'] += 1
eventData[
'Total Wins'] = eventData['Quals Wins'] + eventData['Elims Wins']
eventData['Quals Win %'] = round(
eventData['Quals Wins'] / eventData['Quals Matches'], 3)
eventData['Elims Win %'] = round(
eventData['Elims Wins'] / eventData['Elims Matches'], 3)
eventData['Total Win %'] = round(
eventData['Total Wins'] / eventData['Total Matches'], 3)
climbWins.append(eventData)
climbWins = sorted(climbWins,
key=lambda event: event['Total Win %'],
reverse=True)
outCols = ['Event']
for prefix in ['Quals', 'Elims', 'Total']:
for suffix in [' Wins', ' Matches', ' Win %']:
outCols.append(prefix + suffix)
gen.listOfDictToCSV(dist.upper() + ' Climbing Wins', climbWins, outCols)
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({
'Team': team[3:],
'Rating': rating,
'Record': recordString
})
records = sorted(records, key=lambda k: k['Rating'])
gen.listOfDictToCSV('Worst Records', records, ['Team', 'Rating', 'Record'])
"type": "Event",
"key": "",
"zip": "",
"state": prov,
"country": "Canada",
"district": isDist
})
teams = []
for page in range(0, 20):
gen.progressBar(page, 20)
for team in tba.teams(page, YEAR):
data.append({
"type":
"Team",
"key":
team['key'],
"zip":
handleZip(team),
"state":
team['state_prov'],
"country":
team['country'],
"district": (team['country'] in inDistricts
or team['state_prov'] in inDistricts)
})
colOrder = ["key", "type", "zip", "state", "country", "district"]
gen.listOfDictToCSV(str(YEAR) + "eventLocations", data, colOrder)
import requests
import gen
cdThread = 'https://www.chiefdelphi.com/t/concerns-about-an-off-season-event/358211.json'
data = [{
'Person':
person['name'] if person['name'] != "" else person["username"],
'Posts':
person['post_count'],
'Link':
'http://www.chiefdelphi.com/u/' + person['username']
} for person in requests.get(cdThread).json()['details']['participants']]
gen.listOfDictToCSV('Girls Thread Posters', data, ['Person', 'Posts', 'Link'])
if len(matches) > 0:
winPercent = wins / sum([wins, losses, ties]) * 100
awardCount = 0
awardString = ""
awards = gen.readTeamCsv(team, 'awards', currentYear)
if awards is not None:
awardCount = len(awards)
for idx, award in awards.iterrows():
tail = " / "
if idx + 1 == len(awards):
tail = ""
awardString += award['Name'] + tail
if winPercent > 0:
rookies.append({
'Team': team,
'# of Awards': awardCount,
'Avg OPR': avgOpr,
'Avg Rank': avgRank,
'Wins': wins,
'Win %': winPercent,
'Awards': awardString
})
colOrder = [
'Team', 'Avg OPR', 'Avg Rank', 'Wins', 'Win %', '# of Awards', 'Awards'
]
gen.listOfDictToCSV("rookieData" + str(rookieYear), rookies, colOrder)
draftData = pd.read_excel('Waiver Lists.xlsx')
waiver2 = pd.read_csv('Waiver2Teams.csv')
waiver2.columns = cols
waiverTeams = []
for col in tqdm(draftData.columns):
draftData[col] = draftData[col].fillna(-1).astype(int).astype(str)
key = col
baseTeams = []
if "!" in key:
pass
#Skipping districts for now.
#key = key.strip("!")
#baseTeams = tba.district_teams(str(2019)+key.lower(), False, True)
else:
baseTeams = tba.event_teams(key, False, True)
currentTeams = [team[3:] for team in baseTeams]
for team in currentTeams:
if not draftData[col].str.contains(team).any():
if int(team) not in waiver1['Team'].values or int(
team) not in waiver2['Team'].values:
waiverTeams.append({'Key': key, 'Team': team})
newTeams = len(waiverTeams)
print(newTeams, 'new teams found for waivers.')
if newTeams > 0:
gen.listOfDictToCSV('Waiver3Teams', waiverTeams, cols)
cmpData.append(teamData)
finalData = []
for entry in cmpData:
finalDataEntry = {
'Team': entry['Team'],
'Max OPR': entry['Max OPR'],
'Avg OPR': entry['Avg OPR'],
'Wins': entry['Wins'],
'Losses': entry['Losses'],
'Ties': entry['Ties'],
'Win %': entry['Win %'],
'Avg Total Pts': stat.mean(entry['total']),
'Max Total Pts': max(entry['total']),
'Avg Play Pts': stat.mean(entry['playPts']),
'Max Play Pts': max(entry['playPts']),
'Avg Rank': stat.mean(entry['ranks']),
'Best Rank': min(entry['ranks']),
'Avg Award Pts': stat.mean(entry['awardPts']),
'Max Award Pts': max(entry['awardPts']),
'Awards Won': ' / '.join(entry['awardStrings'])
}
finalData.append(finalDataEntry)
cols = [
'Team', 'Win %', 'Wins', 'Losses', 'Ties', 'Max OPR', 'Avg OPR',
'Max Total Pts', 'Avg Total Pts', 'Avg Rank', 'Best Rank', 'Awards Won',
'Max Play Pts', 'Avg Play Pts', 'Max Award Pts', 'Avg Award Pts'
]
gen.listOfDictToCSV(str(year) + div.upper() + ' Data', finalData, cols, True)
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'])
entCount = 0
gotAward = False
for award in teamAwards:
if award['year'] == year:
awardString = award['event_key'] + " / "
if award['award_type'] in CA:
gotAward = True
caCount += 1
teamRecord[
'wins'] = "CA at " + awardString + teamRecord['wins']
elif award['award_type'] in EI:
gotAward = True
eiCount += 1
teamRecord[
'wins'] = "EI at " + awardString + teamRecord['wins']
elif award['award_type'] in ENT:
gotAward = True
entCount += 1
teamRecord[
'wins'] = "ENT at " + awardString + teamRecord['wins']
if gotAward:
print("Award in " + str(year))
teamRecord['rating'] += (caCount * CA_WEIGHT +
eiCount * EI_WEIGHT +
entCount * ENT_WEIGHT) / (YEAR - year + 1)
winners.append(teamRecord)
gen.listOfDictToCSV(DISTRICT.upper() + "caRatings", winners)
'longStart': 0,
'longEnd': 0
}
for year in tba.team_years(team['key']):
eventWinner = False
for award in tba.team_awards(team['key'], year):
if award['award_type'] == awardTypes.WINNER:
eventWinner = True
if eventWinner:
if teamData['current'] == 0:
teamData['currStart'] = year
teamData['current'] += 1
teamData['currEnd'] = year
if teamData['current'] > teamData['longest']:
teamData['longest'] = teamData['current']
teamData['longStart'] = teamData['currStart']
teamData['longEnd'] = teamData['currEnd']
else:
teamData['current'] = 0
teamData['currStart'] = year
teamData['currEnd'] = year
teamStreaks.append(teamData)
gen.listOfDictToCSV(distKey.upper() + ' Streaks', teamStreaks, [
'team', 'currStart', 'currEnd', 'current', 'longStart', 'longEnd',
'longest'
])
baseList = []
finalList = []
for article in articles:
link = article.find('a')
name = link.get('title')
url = link.get('href')
if name not in baseList:
baseList.append(name)
cleanedName = name.replace('Team ',
'').replace(' ' + str(year), '')
finalList.append({'Team': cleanedName, 'Link': url})
return finalList
teams2018 = fetchYearTeams(2018)
teams2017 = fetchYearTeams(2017)
names2017 = [team['Team'] for team in teams2017]
for team in teams2018:
in2017 = team['Team'] in names2017
team['In 2017'] = ''
if in2017:
team['In 2017'] = 'Yes'
teams2018 = sorted(teams2018, key=lambda k: k['Team'])
gen.listOfDictToCSV('FGC 2018 Teams', teams2018, ['Team', 'Link', 'In 2017'])
import gen
tba = gen.setup()
year = 2019
nameMap = []
for event in tba.events(year):
if event['event_type'] == 0:
nameMap.append({
'Name': event['name'],
'Code': str(2019) + event['event_code']
})
gen.listOfDictToCSV(str(year) + ' Event Name Map', nameMap, ['Name', 'Code'])
import gen
tba = gen.setup()
distTeams = tba.district_teams('2018chs')
teamList = []
for team in distTeams:
if team['state_prov'] == 'Virginia':
teamList.append({'Team': team['key'][3:], 'Sponsors': team['name'].replace(',', ''), 'Location': team['city'] + ' ' + team['state_prov'] + ' ' + str(team['postal_code']) })
gen.listOfDictToCSV('Virginia Team Locations', teamList, ['Team', 'Location', 'Sponsors'])
"245", "1732", "302", "1718", "2337", "51", "4130", "1504", "503", "6538",
"1", "3322", "4395", "240", "4390", "1528", "5708"
]
team_data = []
for team in teams:
vals = []
if team[-1] == 'B':
team_val = 'frc' + team[:-1]
else:
team_val = 'frc' + team
for event in tba.team_events(team_val, 2019):
if event.event_type in eventTypes.NORMAL:
try:
vals.append(tba.event_oprs(event.key)['dprs'][team_val])
except KeyError:
pass
team_data.append({
'team': team,
'min dpr': min(vals),
'max dpr': max(vals),
'avg dpr': stat.mean(vals),
'med dpr': stat.median(vals)
})
gen.listOfDictToCSV('qd_2019marc_data', team_data,
['team', 'min dpr', 'max dpr', 'avg dpr', 'med dpr'])
for team in award['recipient_list']:
teamVal = int(team['team_key'][3:])
if teamVal not in preQual:
preQual.append(teamVal)
for event in divisions:
awards = tba.event_awards(event)
for award in awards:
typ = award['award_type']
if typ == 9:
for team in award['recipient_list']:
teamVal = int(team['team_key'][3:])
if teamVal not in preQual:
preQual.append(teamVal)
champsData = []
for team in preQual:
info = tba.team(team)
champsData.append({
'Team': team,
'Champs': info['home_championship']['2018']
})
detCount = 0
for team in champsData:
detCount += team['Champs'] == 'Detroit'
print(detCount, "/", len(champsData), "Detroit teams")
gen.listOfDictToCSV('Prequalified Champs Data', champsData, ['Team', 'Champs'])
import gen
import awardTypes
from tqdm import tqdm
tba = gen.setup()
year = 2019
dist = 'chs'
distKey = str(year) + dist
awards = []
for team in tqdm(tba.district_teams(distKey, False, True)):
for award in tba.team_awards(team):
if award['award_type'] is awardTypes.WINNER:
awards.append({'Team': team[3:],
'Year': award['event_key'][:4],
'Event': award['event_key'][4:],
'Name': award['name']})
gen.listOfDictToCSV(distKey + " Event Wins", awards, ['Team', 'Year', 'Event', 'Name'])
d['prevTeams'] = d['currentTeams']
d['currentTeams'] = []
#MAR validation checking
marTeams = tba.district_teams('2018mar')
for team in marTeams:
if team['team_number'] in outOfMar:
print(team['team_number'], "is actually in MAR")
#Prep table and then save it out
districtTable = []
for d in districtStats:
dist = districtStats[d]['years']
for year in dist:
isDist = dist[year]['dist']
teams = dist[year]['teams']
added = dist[year]['added']
lost = dist[year]['lost']
districtTable.append({
'dist': d,
'year': year,
'isDist': isDist,
'teams': teams,
'added': added,
'lost': lost
})
gen.writeJsonFile('districtGrowth', districtStats)
gen.listOfDictToCSV('districtGrowth', districtTable)
import gen
tba = gen.setup()
tieData = []
YEARS = [2010, 2011, 2012, 2013, 2014, 2016, 2017, 2018]
for year in YEARS:
print("Processing " + str(year))
matchCount = 0
tieCount = 0
for event in tba.events(year, True, False):
if event['event_type'] in range(0, 10):
print("Fetching matches for " + event['key'])
for match in tba.event_matches(event['key'], True):
tieCount += match['winning_alliance'] not in ['red', 'blue']
matchCount += 1
tieData.append({'year': year, 'matches': matchCount, 'ties': tieCount})
gen.listOfDictToCSV("tieData", tieData)
draftPoints = 9 - alliance['number']
except:
pass
#Find points for playing in elims
for match in teamMatches:
if match['comp_level'] != "qm":
if gen.matchResult(team, match) == 'win':
elimPoints += 5
return [teamRank, draftPoints, rankPoints, elimPoints]
teamData = []
for idx, team in enumerate(teams):
gen.progressBar(idx, teamCount)
rank, draft, rankP, elim = calcPoints(team, EVENT)
total = draft + rankP + elim
teamData.append({
'team': team[3:],
'rank': rank,
'total': total,
'rankP': rankP,
'draft': draft,
'elim': elim
})
colOrder = ['team', 'rank', 'total', 'rankP', 'draft', 'elim']
gen.listOfDictToCSV('iriPoints', teamData, colOrder)
import gen
YEAR = 2018
tba = gen.setup()
teams = gen.readTeamListCsv(2018).Teams
def fetchNames(robotList):
print('memes')
robotNames = []
for team in teams:
robots = tba.team_robots(team)
for robot in robots:
if robot.year == 2018:
robotNames.append({
'name': robot.robot_name,
'length': len(robot.robot_name)
})
print(len(robotNames))
robotNames = sorted(robotNames, key=lambda k: k['Length'])
gen.listOfDictToCSV('robotNames', robotNames, ['Length', 'Name'])
maxPages = 20
finalistTeams = []
for i in range(0, maxPages):
gen.progressBar(i, maxPages)
teams = tba.teams(i, None, False, True)
if teams == []:
break
for team in teams:
finalist = 0
for award in tba.team_awards(team):
if award['award_type'] == 2:
finalist += 1
elif award['award_type'] == 1:
break
finalistTeams.append({'Team': team[3:], 'Finalist Count': finalist})
finalistTeams = sorted(finalistTeams,
key=lambda k: k['Finalist Count'],
reverse=True)
colOrder = ['Team', 'Finalist Count']
gen.listOfDictToCSV("finalistTeams", finalistTeams, colOrder)
# Mid Atlantic Robotics Bylaws Article 1 Section 2 define the PA part of MAR as 'the counties of Pennsylvania including Harrisburg, eastward'
# Harrisburg's longitude is listed as -76.884 , I used -77 as it's a round number and gives a ~7 mile margin.
HARRISBURG_WEST_LON = -77
return event['lng'] > HARRISBURG_WEST_LON
data = []
years = range(2011, 2019)
for idx, year in enumerate(years):
gen.progressBar(idx, len(years))
yearData = {'WOW': 0, 'CHS': 0, 'NE': 0, 'MAR': 0, 'ON': 0, 'NY': 0}
yearEvents = tba.events(year)
for event in yearEvents:
if event['event_type'] == 0:
if event['state_prov'] in areas:
yearData['WOW'] += int(event['state_prov'] in WOW)
yearData['CHS'] += int(event['state_prov'] in CHS)
yearData['NE'] += int(event['state_prov'] in NE)
yearData['MAR'] += int(event['state_prov'] in MAR)
yearData['ON'] += int(event['state_prov'] == 'ON')
yearData['NY'] += int(event['state_prov'] == 'NY')
if event['state_prov'] == 'PA':
yearData['MAR'] += int(inMAR(event))
yearData['WOW'] += int(not inMAR(event))
data.append(yearData)
gen.listOfDictToCSV("neRegionals", data)
