How Does it Work?
The tool works by converting the inputted scores to standardized Z-Scores, then converting those Z-Scores back into other sports. To do this data was collected for each sport in the tool. Data includes:
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NFL - Every outcome of every game in NFL History after 1940
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NBA - Every outcome of every game in NBA history after 1950
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NHL - Every outcome of every game in NHL history 1918
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MLB - Every outcome of every game in MLB history since 1871
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Soccer - Every outcome of every World Cup match since 1930​
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NCAAF - Every NCAA Football game since 1969
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Using this data, 4 values were calculated for each sport:
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The average score of a winning team
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The average score of a losing team
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The sample standard deviation of scores for winning teams
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The sample standard deviation of scores for losing teams
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All data was assumed to be normally distributed. The following figures show the probability distributions of winning and losing scores for each of the 6 sports
The inputted scores are then converted to a Z-Score: (inputted score - average corresponding score) / (standard deviation of the corresponding score). This Z-Score is then used to find the associated expected score in another sport.
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There are some issues
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0 points - though it is technically impossible to win with a score of zero, by assuming a normal distribution, the winning team may be assigned a value of 0 (this is visualized in the above figures)
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Flipping outcomes - at extremely high scores or very close scores, the outcome will change. This is due to the winner "underperforming" based on the distribution and the loser "overachieving"
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Rounding - I chose to not display factional scores, so some scores are closer or not as close as the simulation actually says. For example, a score of 1.1 - 2.9 and a score of 1.4 to 2.6 will both display as 1-3, though the exected win differential is 0.6 greater in the first case.
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This is just for fun, so don't take any results too seriously.
