get_new_players(cplayers, period, prev)
# Update RDs since a period has passed
for cp in cplayers.values():
decay_dev(cp)
# Collect match information
num_games = get_matches(cplayers, period)
print('Initialized: {0} players and {1} games. Updating...'.format(len(cplayers), num_games))
# Update ratings
num_retplayers = 0
num_newplayers = 0
for cp in cplayers.values():
(newr, newd, compr, compd) = update(cp.get_rating_array(), cp.get_dev_array(),\
array(cp.oppr), array(cp.oppd), array(cp.oppc), array(cp.W), array(cp.L),\
cp.player.tag, False)
#break
cp.new_rating = array_to_dict(newr)
cp.new_dev = array_to_dict(newd)
cp.comp_rating = array_to_dict(compr)
cp.comp_dev = array_to_dict(compd)
# Count player as returning or new
if len(cp.W) > 0 and cp.prev_rating_obj:
num_retplayers += 1
elif len(cp.W) > 0:
num_newplayers += 1
#sys.exit(0)
# Get a table of existing rating objects
players[m.plb_id]["wins"].append(m.scb * weight)
players[m.plb_id]["losses"].append(m.sca * weight)
ngames += m.sca + m.scb
# }}}
print("[%s] Initialized %i players and %i games" % (str(datetime.now()), len(players), ngames), flush=True)
# {{{ Compute new ratings, devs and performances
for p in players.values():
new_ratings, new_devs = update(
array([p["prev_ratings"]["M"], p["prev_ratings"]["P"], p["prev_ratings"]["T"], p["prev_ratings"]["Z"]]),
array([p["prev_devs"]["M"], p["prev_devs"]["P"], p["prev_devs"]["T"], p["prev_devs"]["Z"]]),
array(p["opp_r"]),
array(p["opp_d"]),
array(p["opp_c"]),
array(p["wins"]),
array(p["losses"]),
p["player"].tag,
False,
)
perfs = performance(
array(p["opp_r"]),
array(p["opp_d"]),
array(p["opp_c"]),
array(p["wins"]),
array(p["losses"]),
p["player"].tag,
False,
)
))
players[m.plb_id]['wins'].append(m.scb * weight)
players[m.plb_id]['losses'].append(m.sca * weight)
ngames += m.sca + m.scb
# }}}
print('[%s] Initialized %i players and %i games' % (str(datetime.now()), len(players), ngames), flush=True)
# {{{ Compute new ratings, devs and performances
for p in players.values():
new_ratings, new_devs = update(
array(
[p['prev_ratings']['M'], p['prev_ratings']['P'],
p['prev_ratings']['T'], p['prev_ratings']['Z']]
),
array([p['prev_devs']['M'], p['prev_devs']['P'], p['prev_devs']['T'], p['prev_devs']['Z']]),
array(p['opp_r']), array(p['opp_d']), array(p['opp_c']),
array(p['wins']), array(p['losses']), p['player'].tag, False
)
perfs = performance(
array(p['opp_r']), array(p['opp_d']), array(p['opp_c']), array(p['wins']), array(p['losses'])
)
p.update({
'new_ratings': { 'M': new_ratings[0], 'P': new_ratings[1], 'T': new_ratings[2], 'Z': new_ratings[3] },
'new_devs': { 'M': new_devs[0], 'P': new_devs[1], 'T': new_devs[2], 'Z': new_devs[3] },
'perfs': { 'M': perfs[0], 'P': perfs[1], 'T': perfs[2], 'Z': perfs[3] },
})
# }}}
players[m.plb_id]['losses'].append(m.sca * weight)
ngames += m.sca + m.scb
# }}}
print('[%s] Initialized %i players and %i games' %
(str(datetime.now()), len(players), ngames),
flush=True)
# {{{ Compute new ratings, devs and performances
for p in players.values():
new_ratings, new_devs = update(
array([
p['prev_ratings']['M'], p['prev_ratings']['P'],
p['prev_ratings']['T'], p['prev_ratings']['Z']
]),
array([
p['prev_devs']['M'], p['prev_devs']['P'], p['prev_devs']['T'],
p['prev_devs']['Z']
]), array(p['opp_r']), array(p['opp_d']), array(p['opp_c']),
array(p['wins']), array(p['losses']), p['player'].tag, False)
perfs = performance(array(p['opp_r']),
array(p['opp_d']), array(p['opp_c']), array(p['wins']),
array(p['losses']), p['player'].tag, False)
p.update({
'new_ratings': {
'M': new_ratings[0],
'P': new_ratings[1],
'T': new_ratings[2],
'Z': new_ratings[3]
