# Kaggle's NCAA competition¶

Kaggle is a company that runs competitions in Data Science. For the seventh year in a row, they're running an NCAA tourney competition - one for the men's bracket and one for the women's bracket. These are competitions that I've entered several times. This notebook shows one basic strategy to optimize our EigenRanking strategy to generate a submission.

## A description of the competition¶

This is not the run of the mill, office or CBS type competition. You are asked, not just to predict winners, but to associate a probability with each prediction. To enter, you create a file; the first few lines should look something like so:

id,pred
2020_1112_1122,0.96
2020_1112_1124,0.82



This states that for this year, you believe that the team with ID 1112 will beat the team with ID 1122 with probability 0.96. Thus, you're quite confident in that prediction. You're somewhat less confident that they'll beat the team with ID 1124. Note that you need to predict every possible game before the tournament starts (excluding the first four). Thus your entry should have

$$\left(\begin{array}{c}64\\2\end{array}\right)+1 = 2017$$

rows. You can enter before the first four begins, in which case your file will be a bit longer.

Your entry is scored using a LogLoss computation:

$$\textrm{LogLoss} = - \frac{1}{n} \sum_{i=1}^n \left[ y_i \log(\hat{y}_i) + (1 - y_i) \log(1 - \hat{y}_i)\right],$$

where the sum is performed after the fact using only those games that have actually been played and

• $n$ is the number of games played,
• $\hat{y}_i$ is the predicted probability of team 1 beating team 2,
• $y_i$ is 1 if team 1 wins, 0 if team 2 wins, and
• $\log$ is the natural logarithm.

You can take a look at several concrete examples on my Kaggle Brackets page.

The approach that we describe below depends upon a number of parameters; those parameters all affect the matrix used in our EigenRanking technique. Thus, we'll try to optimize that approach against historical data to see if we can find good choices for those parameters.

### Kaggle's data¶

Kaggle provides a ton of data. Among the data files they provide are:

• MTeams.csv - a list of teams with unique IDs referenced in other files.
• MRegularSeasonCompactResults.csv - Results on over 150000 games played 1985-2019. Includes who played, who won, who's court, the score, and number of overtimes.
• MNCAATourneyCompactResults.csv - Results from the NCAA tournament for all games played 1985-2019.

The M prefixed to every file name indicates that we are dealing with the men's tournament; there are similar files prefixed with a W for the women's tournament. All this data is available via the "Data" link at the top of the competition pages, though, you have to be signed in to actually download it. You can sign in via Google or Facebook, so it's not so hard. Either way, none of the code in this notebook will work, unless it lives in the same directory as the DataFiles folder provided by Kaggle.

### Code¶

Let's get to it!

#### Imports¶

In [1]:
## You'll just need to execute this once.

# Standard
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np

# Time a lengthy computation
from time import time

# A tool for getting data
import pandas as pd

# A sparse matrix representation
from scipy.sparse import dok_matrix

# An eigenvalue computer for sparse matrices
from scipy.sparse.linalg import eigs

# We'll need to optimize our procedure.
from scipy.optimize import minimize


#### Reading in and setting up the data for the basic eigen-computation¶

We'll use Pandas' read_csv command to read in the data. We'll store necessary stuff in some dictionaries for quick access.

In [2]:
## You'll just need to execute this once.

teams = [
{
'team_idx':idx,
'team_id':row['TeamID'],
'team_name':row['TeamName']
}
for (idx,row) in teams_df.iterrows()
]


We'll do the same thing with the game data for a given year, which is a bit more complicated. I'm choosing to explore 2011 - a year where Ohio State happened to be pretty good.

In [3]:
## Read in and set up the season results data for a particular year.
## Note that this is the one and only place we set the year. You'll
## need to rerun this every time you do this for another year.

year = 2011

# Load the regular season data
games = [
{
'day':int(row['DayNum']),
'win_id':row['WTeamID'],
'win_score':int(row['WScore']),
'lose_id':row['LTeamID'],
'lose_score':int(row['LScore']),
'win_loc':row['WLoc'],
'num_ot':row['NumOT']
}
for (idx,row) in results_df.iterrows() if row['Season'] == year
]
min_day = min([game['day'] for game in games])
max_day = max([game['day'] for game in games])

# Load the tournament results data - for scoring purposes
tourney_results = []
for idx,row in tourney_results_df.iterrows():
if row['Season'] == year:
win_team = row['WTeamID']
lose_team = row['LTeamID']
tourney_results.append((win_team,lose_team))


The previous input read in and set up some important data for us. Specifically, games is a list of dictionaries that look like so:

{'day': 7,
'lose_id': '1414',
'lose_score': 65,
'num_ot': '0',
'win_id': '1228',
'win_loc': 'H',
'win_score': 79}



That's just one example that's pretty self-explanatory. Note that the 'win_id' and 'lose_id' refer to numeric strings assigned by Kaggle.

We also set up a list of tourney results that looks like so:

[(1243, 1448),(1291, 1309),(1413, 1300),...]



That just tells us who actually beat who.

Finally, we need a team_dict that quickly gives us basic info about each team, given it's Kaggle ID.

In [4]:
## You'll need to execute this each time you try a different year -
## including running the final version for 2020!

team_dict = {}
for game in games:
win_team = game['win_id']
if win_team in team_dict:
team_dict[win_team]['num_games'] = team_dict[win_team]['num_games']+1
else:
team_dict[win_team] = {'num_games':1}
lose_team = game['lose_id']
if lose_team in team_dict:
team_dict[lose_team]['num_games'] = team_dict[lose_team]['num_games']+1
else:
team_dict[lose_team] = {'num_games':1}
n_games = len(team_dict)
cnt = 0
for team in team_dict:
team_dict[team]['matrix_idx'] = cnt
cnt = cnt+1
pos = [team['team_id'] for team in teams].index(team)
team_dict[team]['name'] = teams[pos]['team_name']
reverse_team_dict = dict([(team_dict[team]['matrix_idx'],team) for team in team_dict])


The first few entries of team_dict look like so:

{'1102': {'matrix_idx': 244, 'name': 'Air Force', 'num_games': 28},
'1103': {'matrix_idx': 166, 'name': 'Akron', 'num_games': 34},
'1104': {'matrix_idx': 45, 'name': 'Alabama', 'num_games': 32}, ... }



I guess that 'name' and 'num_games' are self explanatory. The 'matrix_idx' key tells us how each team is associated with the game matrix.

#### Performing the basic eigen-ranking¶

I guess the very simplest matrix we could use is to place the number of times that team $i$ beat team $j$ in row $i$ and column $j$. The dominant eigenvector then yields a (very rough) ranking of the teams.

In [5]:
## This doesn't need to be run.

M = dok_matrix((len(team_dict),len(team_dict)))
for game in games:
win_team = game['win_id']
win_index = team_dict[win_team]['matrix_idx']
lose_team = game['lose_id']
lose_index = team_dict[lose_team]['matrix_idx']
M[win_index,lose_index] = M[win_index,lose_index] + 1
value, vector = eigs(M, which = 'LM', k=1)
vector = abs(np.ndarray.flatten(vector.real))
order = list(vector.argsort())
order.reverse()
ranking = [(vector[k],team_dict[reverse_team_dict[k]]['name']) for k in order]
ranking[:13]

Out[5]:
[(0.16867596110985947, 'Notre Dame'),
(0.16548109813312892, 'Kansas'),
(0.16291036033371925, 'Ohio St'),
(0.16025252228684994, 'Connecticut'),
(0.15524793848813206, 'Louisville'),
(0.15495917587918884, 'Syracuse'),
(0.15426381874189357, 'Duke'),
(0.1536019475442734, 'Pittsburgh'),
(0.1433932567511028, 'Florida'),
(0.14287474858472665, 'Kentucky'),
(0.1369347883688962, "St John's"),
(0.13224879009282292, 'North Carolina'),
(0.13063596358663734, 'Arizona')]

Remember, unless you've got the newest data, this is for a prior year!

### Interpreting our rankings as probabilities¶

Super! But how can we use this to compute probabilities? For example, what's the probability that Ohio State beats the $208^{\text{th}}$ team, who happens to be UNCA?

In [6]:
## This doesn't need to be run.

team208_matrix_idx = order[208]
team208_kaggle_idx = reverse_team_dict[team208_matrix_idx]
team_dict[team208_kaggle_idx]['name']

Out[6]:
'UNC Asheville'

If team 1 and team 2 have strengths $s_1$ and $s_2$, then a very simple and natural approach might be to use

$$\frac{s_1}{s_1+s_{2}}.$$

While this makes some sense in that it at least satisfies the basics of probability theory, it yields the following probability estimate that UNC would've beaten UNCA last year.

In [7]:
## This doesn't need to be run.

team2_matrix_idx = order[2]
team2_strength = vector[team2_matrix_idx]
team208_strength = vector[team208_matrix_idx]
team2_strength/(team2_strength + team208_strength)

Out[7]:
0.8834500944426026

Clearly, the probability should be a bit higher - well over 90% and maybe 98 or 99%. I propose that we scale the probabilities with a function that looks something like so:

In [8]:
## This doesn't need to be run.

def scale(s,x):
if x<=0.5:
return 0.5*(2*x)**(1/s)
else:
return 1-0.5*(2*(1-x))**(1/s)

xs = np.linspace(0,1,200)
for s in (1,0.5,0.25,0.0358):
plt.plot(xs,[scale(s,x) for x in xs])


Thus, our previous computation might become something like so:

In [9]:
## This doesn't need to be run.

scale(0.3, team2_strength/(team2_strength + team208_strength))

Out[9]:
0.9961025871772315

Of course, a reasonable value of $s$ is tweakable. Regardless, I emphasize that this is just a simple, intuitive tweak. A better approach would be to compare the correlation between eigen-strenghts and actual historical win percentage to obtain something like a $p$-value for our prediction. Maybe next year!

On the other hand, from the numerical analysis perspective, we might ignore the "probability" interpretation and simply view this as a minimization of the LogLoss with scale's s parameter as one of the parameters in the minimization.

### A more detailed game matrix¶

Of course, the big question is - what exactly should the game matrix be in terms of all the information at our disposal? If $M=(m_{ij})$, I guess we need formulae for $m_{ij}$ and $m_{ji}$ expressed in terms of the results of the games played between team $i$ and team $j$. The exact contribution to $m_{ij}$ from one particular game might depend on a number of factors, such as

• The winner and loser
• The score
• When the game was played
• Where the game was plaed
• The total number of games played by each team

Of course, there are many other factors that might be considered, but the factors above are the ones we consider here and they are all in Kaggle's data. The specific formua we use below to determine the contribution of one game between team $i$ and team $j$ looks like

$$aw \times dw \frac{ww \times w + sw \times s/t}{n^p},$$

where

• $aw$ is the away weight - i.e., how much an away victory is valued vs a home victory
• $dw$ is the day weight - how much we weight a game in termes of when it was played
• $ww$ is the win weight - how much we weight victory
• $w$ is one or zero depending on whether team $i$ won or lost this game
• $sw$ is the score weight - how much we weight the score
• $s$ is team $i$'s score
• $t$ is the total score
• $n$ is the number of games team $i$ played
• $p$ is a non-negative exponent

### Running a trial¶

The run_trial function defined below is the function that we're going to try to minimize. It's a long function because it incorporates most of the code we've seen to this point and a bit more. It accepts a list of the above parameters, constructs the corresponding game matrix, computes the dominant eigenvector, uses the scale function to produce predicted win probabilities, and uses the known tourney results to compute the corresponding Kaggle score that would have resulted that year. We can optionally get extra information on the results.

In [10]:
## You'll need to rerun this every time you perform optimization for a given year.

def scale(s,x):
if x<=0.5:
return 0.5*(2*x)**(1/s)
else:
return 1-0.5*(2*(1-x))**(1/s)
def run_trial(parameters, extra_info=False):
aw = parameters[0]
sw = parameters[1]
ww = parameters[2]
p = parameters[3]
day_weight = parameters[4]
def dw(day):
return day_weight + (1-day_weight)*(day-min_day)/(max_day-min_day)
s = parameters[5]

M = dok_matrix((len(team_dict),len(team_dict)))
for game in games:
day_weight = dw(game['day'])
win_team = game['win_id']
w_num_games = team_dict[win_team]['num_games']**p
win_score = int(game['win_score'])
win_index = team_dict[win_team]['matrix_idx']
lose_team = game['lose_id']
l_num_games = team_dict[lose_team]['num_games']**p
lose_score = int(game['lose_score'])
lose_index = team_dict[lose_team]['matrix_idx']
total_score = win_score+lose_score
if game['win_loc'] == 'H':
whw = 1
lhw = aw
elif game['win_loc'] == 'A':
whw = aw
lhw = 1
else:
whw = 1
lhw = 1
M[win_index,lose_index] = M[win_index,lose_index] + whw*day_weight*ww/w_num_games + \
(whw*day_weight*sw*win_score/total_score)/w_num_games
M[lose_index,win_index] = M[lose_index,win_index] +  \
(lhw*day_weight*sw*lose_score/total_score)/l_num_games
value, vector = eigs(M, which = 'LM', k=1)
vector = abs(np.ndarray.flatten(vector.real))
order = list(vector.argsort())
order.reverse()

def prob1beats2(team1,team2):
strength1 = vector[team_dict[team1]['matrix_idx']]
strength2 = vector[team_dict[team2]['matrix_idx']]
return scale(s, strength1/(strength1+strength2))

total = 0
cnt = 0
for result in tourney_results:
total = total + np.log(prob1beats2(result[0],result[1]))
cnt = cnt+1
score = -total/cnt
if extra_info == True:
extra_info_dict = {
'ranking_vector': vector,
'order': order,
'p_function': prob1beats2,
'ranking': [team_dict[reverse_team_dict[k]]['name'] for k in order]
}
return score, extra_info_dict
else:
return score


Note that parameters is the list of input parameters. We can use them like so:

In [11]:
## This doesn't need to be run.

# Give a little boost to an away win
aw = 1.3

# Value wins more than scores
sw = 1
ww = 1.5

# Regular normalization per Keener
p = 1

# Games from the beginning of the season count only 3/4 as much as current games.
dw = 0.75

# Probability scaler
s = 0.1

# Run it!
run_trial([aw,sw,ww,p,dw,s])

Out[11]:
0.6654922159101573

Here's what it looks like if you ask for the extra_info:

In [12]:
## This doesn't need to be run.

result, info = run_trial([aw,sw,ww,p,dw, s], extra_info = True)


Thus, for example, we can use the function stored in info['p_function'] to compute the probability that UNC beats UNCA.

In [13]:
## This doesn't need to be run.

osu_kaggle_idx = 1326
unca_kaggle_idx = 1421
p_fun = info['p_function']
p_fun(osu_kaggle_idx, unca_kaggle_idx)

Out[13]:
0.9886600892771993

And we can score this p_function against the actual tourney results that we read in earlier.

In [14]:
## This doesn't need to be run.

total = 0
cnt = 0
for result in tourney_results:
total = total + np.log(p_fun(result[0],result[1]))
cnt = cnt+1
score = -total/cnt
score

Out[14]:
0.6654922159101568

Remember that randomly assigning each team a 50/50 chance results in a score of $\log(2)\approx0.693$, so I guess we're getting better. To improve it more, let's try SciPy's optimize.minimize function. This takes an unfortunate amount of time, but will (hopefully) improve the reult.

In [15]:
## Here's where we actually find parameters to optimize the procedure!!

aw = 1.3; sw = 1; ww = 1.5; p = 1; dw = 0.75; s = 0.1
t = time()
min_result = minimize(run_trial, [aw,sw,ww,p,dw,s],
bounds =  [(1,None), (0.1, None), (0, None), (0,None), (0,1),(0.01,1)])
time()-t

Out[15]:
157.3354778289795

Takes a minute or two on my machine, depending on the year. Let's examine the result.

In [16]:
## This is the important output that you want from the optimization!!

min_result

Out[16]:
      fun: 0.5793753005626135
hess_inv: <6x6 LbfgsInvHessProduct with dtype=float64>
jac: array([ 1.99840144e-07, -1.99840144e-07, -1.88737914e-07,  4.66293670e-07,
2.01039185e-03,  1.64313008e-06])
nfev: 343
nit: 32
status: 0
success: True
x: array([1.17275971, 4.93095709, 0.35154132, 0.56373334, 0.        ,
0.06321605])

The result is essentially a dict with some extra methods for formatting. Of particular interest are the fun and x keys. Of course, we like that success is True! The contents of result['fun'] tells us the minimum that was found. Since this is about 0.579, we see that we've improved on our initial attempt. Since $x$ is so often used as a generic input variable, result['x'] tells us the input to achieve the minimum. Thus, the mimum was achieved with input parameters of about

[1.17, 4.93, 0.35, 0.56, 0.0, 0.63]



Referring back to the purposes of these parameters, we find that home vs away matters a bit, that scores count considerably more than just wins, that the normalization is by about the square root of the number of games, that date played is ignored, and that the probability scaling factor is about $0.63$.

Let's run the trial with these new parameters and check out the rankings:

In [17]:
## This doesn't need to be run, but examining the top 10 might
## help ensure that your results are sensible

result, info = run_trial(min_result['x'], extra_info = True)
info['ranking'][:10]

Out[17]:
['Ohio St',
'Duke',
'Syracuse',
'Louisville',
'Connecticut',
'Kansas',
'Purdue',
'Notre Dame',
'Villanova',
'Cincinnati']

Nice!

### Leakage¶

The technique described so far, suffers severely from a problem called leakage. Essentially, we've optimized specifically for this one year, hoping that the parameters we find work well more generally. We say that the actual results have leaked into our computation. A better approach might be to minimize the sum of computations over many years. While there is still leakage, it's effects should be mitigated.

## Creating a submission file¶

If you want to create a submission file, be sure to run all the code from here after you rerun everything above for 2020.

The filetype that Kaggle expects is detailed on their evaluation page. The first few lines should look something like so:

id,pred
2020_1112_1114,0.91
2020_1112_1122,0.96
2020_1112_1124,0.82



We can create such a file as follows. First, we load the tourney seeds for this year, so we know who's in the tournament and so we can use our historical win probability function as part of our computation.

In [18]:
seeds_df = pd.read_csv('KaggleData/MNCAATourneySeeds.csv')
seed_dict = {};
for (idx,row) in list(seeds_df.iterrows()):
if row['Season'] == year:
team = row['TeamID']
seed = int(row['Seed'][1:3])
seed_dict[team] = seed
teams_in = list(seed_dict.keys())
teams_in.sort()
pairs = [(teams_in[i], teams_in[j])
for i in range(len(teams_in))
for j in range(i+1,len(teams_in))]


Rather than a run_trial function that computes our score (which we have no way of knowing yet), we write a run_it function that returns the p_function and rankings given particular input parameters.

In [19]:
def scale(s,x):
if x<=0.5:
return 0.5*(2*x)**(1/s)
else:
return 1-0.5*(2*(1-x))**(1/s)
def run_it(parameters):
aw = parameters[0]
sw = parameters[1]
ww = parameters[2]
p = parameters[3]
day_weight = parameters[4]
def dw(day):
return day_weight + (1-day_weight)*(day-min_day)/(max_day-min_day)
s = parameters[5]

M = dok_matrix((len(team_dict),len(team_dict)))
for game in games:
day_weight = dw(game['day'])
win_team = game['win_id']
w_num_games = team_dict[win_team]['num_games']**p
win_score = int(game['win_score'])
win_index = team_dict[win_team]['matrix_idx']
lose_team = game['lose_id']
l_num_games = team_dict[lose_team]['num_games']**p
lose_score = int(game['lose_score'])
lose_index = team_dict[lose_team]['matrix_idx']
total_score = win_score+lose_score
if game['win_loc'] == 'H':
whw = 1
lhw = aw
elif game['win_loc'] == 'A':
whw = aw
lhw = 1
else:
whw = 1
lhw = 1
M[win_index,lose_index] = M[win_index,lose_index] + whw*day_weight*ww/w_num_games + \
(whw*day_weight*sw*win_score/total_score)/w_num_games
M[lose_index,win_index] = M[lose_index,win_index] +  \
(lhw*day_weight*sw*lose_score/total_score)/l_num_games
value, vector = eigs(M, which = 'LM', k=1)
vector = abs(np.ndarray.flatten(vector.real))
order = list(vector.argsort())
order.reverse()

def prob1beats2(team1,team2):
strength1 = vector[team_dict[team1]['matrix_idx']]
strength2 = vector[team_dict[team2]['matrix_idx']]
return scale(s, strength1/(strength1+strength2))

team_dicts = [team_dict[reverse_team_dict[k]] for k in order]
for idx,td in enumerate(team_dicts):
td['rating'] = vector[order][idx]

return {
'ranking_vector': vector,
'order': order,
'p_function': prob1beats2,
'rankings': [team_dict[reverse_team_dict[k]] for k in order]
}


Here's an example and the resulting rankings.

In [20]:
attempt = run_it([1.1, 1.0, 0.01, 0.9, 1.0, 0.025])
[x['name'] for x in attempt['rankings'][:10]]

Out[20]:
['Ohio St',
'Duke',
'Kansas',
'Pittsburgh',
'Purdue',
'Syracuse',
'Texas',
'Kentucky',
'Louisville',
'San Diego St']

Finally, the following code will create your submission file called submit.csv.

In [21]:
p_fun = attempt['p_function']
file_handle = open('submit.csv', 'w')
file_handle.write("id,pred\n")
for pair in pairs:
line = str(year) + "_" + str(pair[0]) + "_" + str(pair[1]) + ","
p = p_fun(pair[0], pair[1])
p = str(p)
line = line + p
#line = line + ",\t" + team_dict[pair[0]]['name'] + " - " + team_dict[pair[1]]['name']
line = line + "\n"
file_handle.write(line)
file_handle.close()