Change rating system (DMOJ)

.gitignore

@@ -15,4 +15,5 @@ sass_processed
 <desired bridge log path>
 node_modules/
 package-lock.json
+/src
 

judge/migrations/0118_rating.py

@@ -0,0 +1,208 @@
+import math
+from operator import attrgetter, itemgetter
+
+from django.db import migrations, models
+from django.db.models import Count, OuterRef, Subquery
+from django.db.models.functions import Coalesce
+from django.utils import timezone
+
+
+def tie_ranker(iterable, key=attrgetter('points')):
+    rank = 0
+    delta = 1
+    last = None
+    buf = []
+    for item in iterable:
+        new = key(item)
+        if new != last:
+            for _ in buf:
+                yield rank + (delta - 1) / 2.0
+            rank += delta
+            delta = 0
+            buf = []
+        delta += 1
+        buf.append(item)
+        last = key(item)
+    for _ in buf:
+        yield rank + (delta - 1) / 2.0
+
+
+def rational_approximation(t):
+    # Abramowitz and Stegun formula 26.2.23.
+    # The absolute value of the error should be less than 4.5 e-4.
+    c = [2.515517, 0.802853, 0.010328]
+    d = [1.432788, 0.189269, 0.001308]
+    numerator = (c[2] * t + c[1]) * t + c[0]
+    denominator = ((d[2] * t + d[1]) * t + d[0]) * t + 1.0
+    return t - numerator / denominator
+
+
+def normal_CDF_inverse(p):
+    assert 0.0 < p < 1
+
+    # See article above for explanation of this section.
+    if p < 0.5:
+        # F^-1(p) = - G^-1(p)
+        return -rational_approximation(math.sqrt(-2.0 * math.log(p)))
+    else:
+        # F^-1(p) = G^-1(1-p)
+        return rational_approximation(math.sqrt(-2.0 * math.log(1.0 - p)))
+
+
+def WP(RA, RB, VA, VB):
+    return (math.erf((RB - RA) / math.sqrt(2 * (VA * VA + VB * VB))) + 1) / 2.0
+
+
+def recalculate_ratings(old_rating, old_volatility, actual_rank, times_rated, is_disqualified):
+    # actual_rank: 1 is first place, N is last place
+    # if there are ties, use the average of places (if places 2, 3, 4, 5 tie, use 3.5 for all of them)
+
+    N = len(old_rating)
+    new_rating = old_rating[:]
+    new_volatility = old_volatility[:]
+    if N <= 1:
+        return new_rating, new_volatility
+
+    ranking = list(range(N))
+    ranking.sort(key=old_rating.__getitem__, reverse=True)
+
+    ave_rating = float(sum(old_rating)) / N
+    sum1 = sum(i * i for i in old_volatility) / N
+    sum2 = sum((i - ave_rating) ** 2 for i in old_rating) / (N - 1)
+    CF = math.sqrt(sum1 + sum2)
+
+    for i in range(N):
+        ERank = 0.5
+        for j in range(N):
+            ERank += WP(old_rating[i], old_rating[j], old_volatility[i], old_volatility[j])
+
+        EPerf = -normal_CDF_inverse((ERank - 0.5) / N)
+        APerf = -normal_CDF_inverse((actual_rank[i] - 0.5) / N)
+        PerfAs = old_rating[i] + CF * (APerf - EPerf)
+        Weight = 1.0 / (1 - (0.42 / (times_rated[i] + 1) + 0.18)) - 1.0
+        if old_rating[i] > 2500:
+            Weight *= 0.8
+        elif old_rating[i] >= 2000:
+            Weight *= 0.9
+
+        Cap = 150.0 + 1500.0 / (times_rated[i] + 2)
+
+        new_rating[i] = (old_rating[i] + Weight * PerfAs) / (1.0 + Weight)
+
+        if abs(old_rating[i] - new_rating[i]) > Cap:
+            if old_rating[i] < new_rating[i]:
+                new_rating[i] = old_rating[i] + Cap
+            else:
+                new_rating[i] = old_rating[i] - Cap
+
+        if times_rated[i] == 0:
+            new_volatility[i] = 385
+        else:
+            new_volatility[i] = math.sqrt(((new_rating[i] - old_rating[i]) ** 2) / Weight +
+                                          (old_volatility[i] ** 2) / (Weight + 1))
+
+        if is_disqualified[i]:
+            # DQed users can manipulate TopCoder ratings to get higher volatility in order to increase their rating
+            # later on, prohibit this by ensuring their volatility never increases in this situation
+            new_volatility[i] = min(new_volatility[i], old_volatility[i])
+
+    # try to keep the sum of ratings constant
+    adjust = float(sum(old_rating) - sum(new_rating)) / N
+    new_rating = list(map(adjust.__add__, new_rating))
+    # inflate a little if we have to so people who placed first don't lose rating
+    best_rank = min(actual_rank)
+    for i in range(N):
+        if abs(actual_rank[i] - best_rank) <= 1e-3 and new_rating[i] < old_rating[i] + 1:
+            new_rating[i] = old_rating[i] + 1
+    return list(map(int, map(round, new_rating))), list(map(int, map(round, new_volatility)))
+
+
+def tc_rate_contest(contest, Rating, Profile):
+    rating_subquery = Rating.objects.filter(user=OuterRef('user'))
+    rating_sorted = rating_subquery.order_by('-contest__end_time')
+    users = contest.users.order_by('is_disqualified', '-score', 'cumtime', 'tiebreaker') \
+        .annotate(submissions=Count('submission'),
+                  last_rating=Coalesce(Subquery(rating_sorted.values('rating')[:1]), 1200),
+                  volatility=Coalesce(Subquery(rating_sorted.values('volatility')[:1]), 535),
+                  times=Coalesce(Subquery(rating_subquery.order_by().values('user_id')
+                                          .annotate(count=Count('id')).values('count')), 0)) \
+        .exclude(user_id__in=contest.rate_exclude.all()) \
+        .filter(virtual=0).values('id', 'user_id', 'score', 'cumtime', 'tiebreaker', 'is_disqualified',
+                                  'last_rating', 'volatility', 'times')
+    if not contest.rate_all:
+        users = users.filter(submissions__gt=0)
+    if contest.rating_floor is not None:
+        users = users.exclude(last_rating__lt=contest.rating_floor)
+    if contest.rating_ceiling is not None:
+        users = users.exclude(last_rating__gt=contest.rating_ceiling)
+
+    users = list(users)
+    participation_ids = list(map(itemgetter('id'), users))
+    user_ids = list(map(itemgetter('user_id'), users))
+    is_disqualified = list(map(itemgetter('is_disqualified'), users))
+    ranking = list(tie_ranker(users, key=itemgetter('score', 'cumtime', 'tiebreaker')))
+    old_rating = list(map(itemgetter('last_rating'), users))
+    old_volatility = list(map(itemgetter('volatility'), users))
+    times_ranked = list(map(itemgetter('times'), users))
+    rating, volatility = recalculate_ratings(old_rating, old_volatility, ranking, times_ranked, is_disqualified)
+
+    now = timezone.now()
+    ratings = [Rating(user_id=i, contest=contest, rating=r, volatility=v, last_rated=now, participation_id=p, rank=z)
+               for i, p, r, v, z in zip(user_ids, participation_ids, rating, volatility, ranking)]
+
+    Rating.objects.bulk_create(ratings)
+
+    Profile.objects.filter(contest_history__contest=contest, contest_history__virtual=0).update(
+        rating=Subquery(Rating.objects.filter(user=OuterRef('id'))
+                        .order_by('-contest__end_time').values('rating')[:1]))
+
+
+# inspired by rate_all_view
+def rate_tc(apps, schema_editor):
+    Contest = apps.get_model('judge', 'Contest')
+    Rating = apps.get_model('judge', 'Rating')
+    Profile = apps.get_model('judge', 'Profile')
+
+    with schema_editor.connection.cursor() as cursor:
+        cursor.execute('TRUNCATE TABLE `%s`' % Rating._meta.db_table)
+    Profile.objects.update(rating=None)
+    for contest in Contest.objects.filter(is_rated=True, end_time__lte=timezone.now()).order_by('end_time'):
+        tc_rate_contest(contest, Rating, Profile)
+
+
+# inspired by rate_all_view
+def rate_elo_mmr(apps, schema_editor):
+    Rating = apps.get_model('judge', 'Rating')
+    Profile = apps.get_model('judge', 'Profile')
+
+    with schema_editor.connection.cursor() as cursor:
+        cursor.execute('TRUNCATE TABLE `%s`' % Rating._meta.db_table)
+    Profile.objects.update(rating=None)
+    # Don't populate Rating
+
+
+class Migration(migrations.Migration):
+
+    dependencies = [
+        ('judge', '0117_auto_20211209_0612'),
+    ]
+
+    operations = [
+        migrations.RunPython(migrations.RunPython.noop, rate_tc, atomic=True),
+        migrations.AddField(
+            model_name='rating',
+            name='mean',
+            field=models.FloatField(verbose_name='raw rating'),
+        ),
+        migrations.AddField(
+            model_name='rating',
+            name='performance',
+            field=models.FloatField(verbose_name='contest performance'),
+        ),
+        migrations.RemoveField(
+            model_name='rating',
+            name='volatility',
+            field=models.IntegerField(verbose_name='volatility'),
+        ),
+        migrations.RunPython(rate_elo_mmr, migrations.RunPython.noop, atomic=True),
+    ]

judge/models/contest.py

@@ -531,7 +531,8 @@ class Rating(models.Model):
                                          related_name='rating', on_delete=CASCADE)
     rank = models.IntegerField(verbose_name=_('rank'))
     rating = models.IntegerField(verbose_name=_('rating'))
-    volatility = models.IntegerField(verbose_name=_('volatility'))
+    mean = models.FloatField(verbose_name=_('raw rating'))
+    performance = models.FloatField(verbose_name=_('contest performance'))
     last_rated = models.DateTimeField(db_index=True, verbose_name=_('last rated'))
 
     class Meta:

judge/ratings.py

@@ -1,5 +1,5 @@
-import math
 from bisect import bisect
+from math import pi, sqrt, tanh
 from operator import attrgetter, itemgetter
 
 from django.db import transaction
@@ -8,6 +8,18 @@ from django.db.models.functions import Coalesce
 from django.utils import timezone
 
 
+BETA2 = 328.33 ** 2
+RATING_INIT = 1200      # Newcomer's rating when applying the rating floor/ceiling
+MEAN_INIT = 1500.
+VAR_INIT = 350**2 * (BETA2 / 212**2)
+SD_INIT = sqrt(VAR_INIT)
+VALID_RANGE = MEAN_INIT - 20 * SD_INIT, MEAN_INIT + 20 * SD_INIT
+VAR_PER_CONTEST = 1219.047619 * (BETA2 / 212**2)
+VAR_LIM = (sqrt(VAR_PER_CONTEST**2 + 4 * BETA2 * VAR_PER_CONTEST) - VAR_PER_CONTEST) / 2
+SD_LIM = sqrt(VAR_LIM)
+TANH_C = sqrt(3) / pi
+
+
 def tie_ranker(iterable, key=attrgetter('points')):
     rank = 0
     delta = 1
@@ -28,94 +40,103 @@ def tie_ranker(iterable, key=attrgetter('points')):
         yield rank + (delta - 1) / 2.0
 
 
-def rational_approximation(t):
+def eval_tanhs(tanh_terms, x):
-    # Abramowitz and Stegun formula 26.2.23.
+    return sum((wt / sd) * tanh((x - mu) / (2 * sd)) for mu, sd, wt in tanh_terms)
-    # The absolute value of the error should be less than 4.5 e-4.
-    c = [2.515517, 0.802853, 0.010328]
-    d = [1.432788, 0.189269, 0.001308]
-    numerator = (c[2] * t + c[1]) * t + c[0]
-    denominator = ((d[2] * t + d[1]) * t + d[0]) * t + 1.0
-    return t - numerator / denominator
 
 
-def normal_CDF_inverse(p):
+def solve(tanh_terms, y_tg, lin_factor=0, bounds=VALID_RANGE):
-    assert 0.0 < p < 1
+    L, R = bounds
-
+    Ly, Ry = None, None
-    # See article above for explanation of this section.
+    while R - L > 2:
-    if p < 0.5:
+        x = (L + R) / 2
-        # F^-1(p) = - G^-1(p)
+        y = lin_factor * x + eval_tanhs(tanh_terms, x)
-        return -rational_approximation(math.sqrt(-2.0 * math.log(p)))
+        if y > y_tg:
-    else:
+            R, Ry = x, y
-        # F^-1(p) = G^-1(1-p)
+        elif y < y_tg:
-        return rational_approximation(math.sqrt(-2.0 * math.log(1.0 - p)))
+            L, Ly = x, y
-
-
-def WP(RA, RB, VA, VB):
-    return (math.erf((RB - RA) / math.sqrt(2 * (VA * VA + VB * VB))) + 1) / 2.0
-
-
-def recalculate_ratings(old_rating, old_volatility, actual_rank, times_rated, is_disqualified):
-    # actual_rank: 1 is first place, N is last place
-    # if there are ties, use the average of places (if places 2, 3, 4, 5 tie, use 3.5 for all of them)
-
-    N = len(old_rating)
-    new_rating = old_rating[:]
-    new_volatility = old_volatility[:]
-    if N <= 1:
-        return new_rating, new_volatility
-
-    ranking = list(range(N))
-    ranking.sort(key=old_rating.__getitem__, reverse=True)
-
-    ave_rating = float(sum(old_rating)) / N
-    sum1 = sum(i * i for i in old_volatility) / N
-    sum2 = sum((i - ave_rating) ** 2 for i in old_rating) / (N - 1)
-    CF = math.sqrt(sum1 + sum2)
-
-    for i in range(N):
-        ERank = 0.5
-        for j in range(N):
-            ERank += WP(old_rating[i], old_rating[j], old_volatility[i], old_volatility[j])
-
-        EPerf = -normal_CDF_inverse((ERank - 0.5) / N)
-        APerf = -normal_CDF_inverse((actual_rank[i] - 0.5) / N)
-        PerfAs = old_rating[i] + CF * (APerf - EPerf)
-        Weight = 1.0 / (1 - (0.42 / (times_rated[i] + 1) + 0.18)) - 1.0
-        if old_rating[i] > 2500:
-            Weight *= 0.8
-        elif old_rating[i] >= 2000:
-            Weight *= 0.9
-
-        Cap = 150.0 + 1500.0 / (times_rated[i] + 2)
-
-        new_rating[i] = (old_rating[i] + Weight * PerfAs) / (1.0 + Weight)
-
-        if times_rated[i] == 0:
-            new_volatility[i] = 385
         else:
-            new_volatility[i] = math.sqrt(((new_rating[i] - old_rating[i]) ** 2) / Weight +
+            return x
-                                          (old_volatility[i] ** 2) / (Weight + 1))
+    # Use linear interpolation to be slightly more accurate.
+    if Ly is None:
+        Ly = lin_factor * L + eval_tanhs(tanh_terms, L)
+    if y_tg <= Ly:
+        return L
+    if Ry is None:
+        Ry = lin_factor * R + eval_tanhs(tanh_terms, R)
+    if y_tg >= Ry:
+        return R
+    ratio = (y_tg - Ly) / (Ry - Ly)
+    return L * (1 - ratio) + R * ratio
 
-        if is_disqualified[i]:
-            # DQed users can manipulate TopCoder ratings to get higher volatility in order to increase their rating
-            # later on, prohibit this by ensuring their volatility never increases in this situation
-            new_volatility[i] = min(new_volatility[i], old_volatility[i])
 
-        if abs(old_rating[i] - new_rating[i]) > Cap:
+def get_var(times_ranked, cache=[VAR_INIT]):
-            if old_rating[i] < new_rating[i]:
+    while times_ranked >= len(cache):
-                new_rating[i] = old_rating[i] + Cap
+        next_var = 1. / (1. / (cache[-1] + VAR_PER_CONTEST) + 1. / BETA2)
-            else:
+        cache.append(next_var)
-                new_rating[i] = old_rating[i] - Cap
+    return cache[times_ranked]
 
-    # try to keep the sum of ratings constant
+
-    adjust = float(sum(old_rating) - sum(new_rating)) / N
+def recalculate_ratings(ranking, old_mean, times_ranked, historical_p):
-    new_rating = list(map(adjust.__add__, new_rating))
+    n = len(ranking)
-    # inflate a little if we have to so people who placed first don't lose rating
+    new_p = [0.] * n
-    best_rank = min(actual_rank)
+    new_mean = [0.] * n
-    for i in range(N):
+
-        if abs(actual_rank[i] - best_rank) <= 1e-3 and new_rating[i] < old_rating[i] + 1:
+    # Note: pre-multiply delta by TANH_C to improve efficiency.
-            new_rating[i] = old_rating[i] + 1
+    delta = [TANH_C * sqrt(get_var(t) + VAR_PER_CONTEST + BETA2) for t in times_ranked]
-    return list(map(int, map(round, new_rating))), list(map(int, map(round, new_volatility)))
+    p_tanh_terms = [(m, d, 1) for m, d in zip(old_mean, delta)]
+
+    # Calculate performance at index i.
+    def solve_idx(i, bounds=VALID_RANGE):
+        r = ranking[i]
+        y_tg = 0
+        for d, s in zip(delta, ranking):
+            if s > r:       # s loses to r
+                y_tg += 1. / d
+            elif s < r:     # s beats r
+                y_tg -= 1. / d
+            # Otherwise, this is a tie that counts as half a win, as per Elo-MMR.
+        new_p[i] = solve(p_tanh_terms, y_tg, bounds=bounds)
+
+    # Fill all indices between i and j, inclusive. Use the fact that new_p is non-increasing.
+    def divconq(i, j):
+        if j - i > 1:
+            k = (i + j) // 2
+            solve_idx(k, bounds=(new_p[j], new_p[i]))
+            divconq(i, k)
+            divconq(k, j)
+
+    if n < 2:
+        new_p = list(old_mean)
+        new_mean = list(old_mean)
+    else:
+        # Calculate performance.
+        solve_idx(0)
+        solve_idx(n - 1)
+        divconq(0, n - 1)
+
+        # Calculate mean.
+        for i, r in enumerate(ranking):
+            tanh_terms = []
+            w_prev = 1.
+            w_sum = 0.
+            for j, h in enumerate([new_p[i]] + historical_p[i]):
+                gamma2 = (VAR_PER_CONTEST if j > 0 else 0)
+                h_var = get_var(times_ranked[i] + 1 - j)
+                k = h_var / (h_var + gamma2)
+                w = w_prev * k**2
+                # Future optimization: If j is around 20, then w < 1e-3 and it is possible to break early.
+                tanh_terms.append((h, sqrt(BETA2) * TANH_C, w))
+                w_prev = w
+                w_sum += w / BETA2
+            w0 = 1. / get_var(times_ranked[i] + 1) - w_sum
+            p0 = eval_tanhs(tanh_terms[1:], old_mean[i]) / w0 + old_mean[i]
+            new_mean[i] = solve(tanh_terms, w0 * p0, lin_factor=w0)
+
+    # Display a slightly lower rating to incentivize participation.
+    # As times_ranked increases, new_rating converges to new_mean.
+    new_rating = [max(1, round(m - (sqrt(get_var(t + 1)) - SD_LIM))) for m, t in zip(new_mean, times_ranked)]
+
+    return new_rating, new_mean, new_p
 
 
 def rate_contest(contest):
@@ -125,13 +146,13 @@ def rate_contest(contest):
     rating_sorted = rating_subquery.order_by('-contest__end_time')
     users = contest.users.order_by('is_disqualified', '-score', 'cumtime', 'tiebreaker') \
         .annotate(submissions=Count('submission'),
-                  last_rating=Coalesce(Subquery(rating_sorted.values('rating')[:1]), 1200),
+                  last_rating=Coalesce(Subquery(rating_sorted.values('rating')[:1]), RATING_INIT),
-                  volatility=Coalesce(Subquery(rating_sorted.values('volatility')[:1]), 535),
+                  last_mean=Coalesce(Subquery(rating_sorted.values('mean')[:1]), MEAN_INIT),
                   times=Coalesce(Subquery(rating_subquery.order_by().values('user_id')
                                           .annotate(count=Count('id')).values('count')), 0)) \
         .exclude(user_id__in=contest.rate_exclude.all()) \
-        .filter(virtual=0).values('id', 'user_id', 'score', 'cumtime', 'tiebreaker', 'is_disqualified',
+        .filter(virtual=0).values('id', 'user_id', 'score', 'cumtime', 'tiebreaker',
-                                  'last_rating', 'volatility', 'times')
+                                  'last_rating', 'last_mean', 'times')
     if not contest.rate_all:
         users = users.filter(submissions__gt=0)
     if contest.rating_floor is not None:
@@ -142,27 +163,34 @@ def rate_contest(contest):
     users = list(users)
     participation_ids = list(map(itemgetter('id'), users))
     user_ids = list(map(itemgetter('user_id'), users))
-    is_disqualified = list(map(itemgetter('is_disqualified'), users))
     ranking = list(tie_ranker(users, key=itemgetter('score', 'cumtime', 'tiebreaker')))
-    old_rating = list(map(itemgetter('last_rating'), users))
+    old_mean = list(map(itemgetter('last_mean'), users))
-    old_volatility = list(map(itemgetter('volatility'), users))
     times_ranked = list(map(itemgetter('times'), users))
-    rating, volatility = recalculate_ratings(old_rating, old_volatility, ranking, times_ranked, is_disqualified)
+    historical_p = [[] for _ in users]
+
+    user_id_to_idx = {uid: i for i, uid in enumerate(user_ids)}
+    for h in Rating.objects.filter(user_id__in=user_ids) \
+            .order_by('-contest__end_time') \
+            .values('user_id', 'performance'):
+        idx = user_id_to_idx[h['user_id']]
+        historical_p[idx].append(h['performance'])
+
+    rating, mean, performance = recalculate_ratings(ranking, old_mean, times_ranked, historical_p)
 
     now = timezone.now()
-    ratings = [Rating(user_id=i, contest=contest, rating=r, volatility=v, last_rated=now, participation_id=p, rank=z)
+    ratings = [Rating(user_id=i, contest=contest, rating=r, mean=m, performance=perf,
-               for i, p, r, v, z in zip(user_ids, participation_ids, rating, volatility, ranking)]
+                      last_rated=now, participation_id=pid, rank=z)
+               for i, pid, r, m, perf, z in zip(user_ids, participation_ids, rating, mean, performance, ranking)]
     with transaction.atomic():
-        Rating.objects.filter(contest=contest).delete()
         Rating.objects.bulk_create(ratings)
-        Profile.objects.filter(contest_history__contest=contest, contest_history__virtual=0) \
+
-                       .update(rating=Subquery(Rating.objects.filter(user=OuterRef('id')) \
+        Profile.objects.filter(contest_history__contest=contest, contest_history__virtual=0).update(
-                       .order_by('-contest__end_time').values('rating')[:1]))
+            rating=Subquery(Rating.objects.filter(user=OuterRef('id'))
-    return old_rating, old_volatility, ranking, times_ranked, rating, volatility
+                            .order_by('-contest__end_time').values('rating')[:1]))
 
 
 RATING_LEVELS = ['Newbie', 'Amateur', 'Expert', 'Candidate Master', 'Master', 'Grandmaster', 'Target']
-RATING_VALUES = [1000, 1200, 1500, 1800, 2200, 3000]
+RATING_VALUES = [1000, 1300, 1600, 1900, 2400, 3000]
 RATING_CLASS = ['rate-newbie', 'rate-amateur', 'rate-expert', 'rate-candidate-master',
                 'rate-master', 'rate-grandmaster', 'rate-target']
 

judge/views/api/api_v1.py

@@ -137,16 +137,17 @@ def api_v1_user_info(request, user):
     participations = ContestParticipation.objects.filter(user=profile, virtual=0, contest__is_visible=True,
                                                          contest__is_private=False,
                                                          contest__is_organization_private=False)
-    for contest_key, rating, volatility in participations.values_list('contest__key', 'rating__rating',
+    for contest_key, rating, mean, performance in participations.values_list(
-                                                                      'rating__volatility'):
+        'contest__key', 'rating__rating', 'rating__mean', 'rating__performance',
+    ):
         contest_history[contest_key] = {
             'rating': rating,
-            'volatility': volatility,
+            'raw_rating': mean,
+            'performance': performance,
         }
 
     resp['contests'] = {
         'current_rating': last_rating.rating if last_rating else None,
-        'volatility': last_rating.volatility if last_rating else None,
         'history': contest_history,
     }
 

judge/views/api/api_v2.py

@@ -89,7 +89,6 @@ def api_v2_user_info(request):
 
     resp['contests'] = {
         "current_rating": last_rating[0].rating if last_rating else None,
-        "volatility": last_rating[0].volatility if last_rating else None,
         'history': contest_history,
     }
 

templates/user/user-about.html

@@ -467,26 +467,26 @@
                             },
                             {
                                 begin: 1000,
-                                end: 1200,
+                                end: 1300,
                                 color: 'rgb(0, 169, 0, 0.4)'
                             },
                             {
-                                begin: 1200,
+                                begin: 1300,
-                                end: 1500,
+                                end: 1600,
                                 color: 'rgb(0, 0, 255, 0.4)'
                             },
                             {
-                                begin: 1500,
+                                begin: 1600,
-                                end: 1800,
+                                end: 1900,
                                 color: 'rgb(128, 0, 128, 0.37)'
                             },
                             {
-                                begin: 1800,
+                                begin: 1900,
-                                end: 2200,
+                                end: 2400,
                                 color: 'rgb(255, 177, 0, 0.4)'
                             },
                             {
-                                begin: 2200,
+                                begin: 2400,
                                 end: 3000,
                                 color: 'rgb(238, 0, 0, 0.4)'
                             },

templates/user/user-base.html

@@ -120,10 +120,6 @@
                         <div class="user-stat-header">{{_('Contests written')}}:</div>
                         <div class="user-stat">{{ratings|length}}</div>
                     </div>
-                    <div class="user-stat-container">
-                        <div class="user-stat-header">{{ _('Volatility:') }}</div>
-                        <div class="user-stat">{{ rating.volatility }}</div>
-                    </div>
                     <div class="user-stat-container">
                         <div class="user-stat-header">{{ _('Min. rating:') }}</div>
                         <div class="user-stat">{{ rating_number(min_rating) }}</div>