Scientific Gambling on Hockey Worlds – Expected profits from games of day 1 & 2

An Expected Value-calculation gives the expected gains from my bets on the games played during the first two days of the tournament as follows:

          OUTCOME  U_ODDS       U_P         P   P_DELTA  EV_PER_UNIT
HOME AWAY
CZE  SVK     DRAW    5.20  0.192308  0.243738  0.051430     0.267438
     SVK     LOSS    5.80  0.172414  0.169599 -0.002814    -0.016324
GER  DEN     DRAW    4.25  0.235294  0.274173  0.038879     0.165237
     DEN     LOSS    3.20  0.312500  0.341512  0.029012     0.092838
NOR  LAT     DRAW    4.00  0.250000  0.291737  0.041737     0.166949
RUS  FRA     DRAW   10.00  0.100000  0.184241  0.084241     0.842407
SUI  AUT     DRAW    5.50  0.181818  0.320461  0.138643     0.762537
     AUT     LOSS    6.75  0.148148  0.147649 -0.000499    -0.003367
SWE  BLR     DRAW    8.00  0.125000  0.209213  0.084213     0.673707
     BLR     LOSS   11.00  0.090909  0.149934  0.059025     0.649274

In the table above, the ‘OUTCOME’ column shows my bet, from the perspective of the ‘home’-team, ‘U_ODDS’ are the odds given by Unibet, ‘U_P’ are those odds converted to a probability, ‘P’ is the probability that my Bayesian model gives to the game, ‘P_DELTA’ the difference in Unibet’s and my programs belief on the outcome probability, and ‘EV_PER_UNIT’ the expected value per unit stake.

For these initial games, the calculated profit margin for my bets is 22%

Below the Expected Value in graphical form.

(The reason FIN-KOR has no bars in the graph is that those two teams have never met before at championship level, and thus not enough data for meaningful inferences)

EV

About swdevperestroika

High tech industry veteran, avid hacker reluctantly transformed to mgmt consultant.
This entry was posted in Bayes, Data Analytics, Data Driven Management, Gambling, HOCKEY-2018, Numpy, Probability, PYMC, Python, Statistics and tagged , , , , , , , , , , , . Bookmark the permalink.

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