Ask The Wizard #247
- Using the total and spread, estimate the total points of each team. For example, if we use a total of 57 and a spread of -5 for the Super Bowl, letting c=Colts points, and s=Saints points then...
(1) c+s=57
(2) c-5=s
Substituting equation (2) in equation (1):
c+(c-5)=57
2c-5=57
2c=62
c=31
s=31-5=26
The problem with stopping here is sometimes you get values that are unlikely to be scored by a single team. For example, the probability of a single-team total of 24 is 6.5%, but the probability of 25 is only 0.9%. The table below shows the single-team total probability, based on the 2000-2009 seasons. So we’re going to estimate the total points for each team based on realistic combinations of field goals and touchdowns.
- Assume the favorite kicks 2 field goals.
- Assume the underdog kicks 1 field goal.
- Subtract the field goal points from each. In the Super Bowl example, this would leave Colts = 25 touchdown points, Saints = 23 touchdown points.
- Divide touchdown points by 7, to get estimated touchdowns. c=3.57 TD, s=3.29 TD
- Round the estimated touchdowns to the nearest integer. c=4, s=3.
- Following this method, we get for total points c=(4×7)+(2×3)=34, s=(3×7)+(1×3)=24.
Using this method on all 6,707 games from the 1983 through the 2009 seasons would have resulted in 69 correct picks, for a success rate of 1.03%. The last time it would have been right was the Titans/Colts game in week 13 of 2009. That game had a spread of Colts -6.5, and a total of 46. The score was Titans 17, Colts 27.
One critic thought a better and simpler strategy would be to pick the nearest significant one-team total for both teams. Using such a method resulted in 51 wins only, for a win rate of 0.76%. In my opinion, splitting the field goals 2 and 1 between the stronger and weaker teams is important.Single-Team Totals in the NFL2000-2009 Seasons
| One-Team Total | Total in Sample | Probability |
| 0 | 93 | 1.75% |
| 1 | 0 | 0.00% |
| 2 | 0 | 0.00% |
| 3 | 148 | 2.79% |
| 4 | 0 | 0.00% |
| 5 | 2 | 0.04% |
| 6 | 114 | 2.15% |
| 7 | 210 | 3.96% |
| 8 | 9 | 0.17% |
| 9 | 76 | 1.43% |
| 10 | 316 | 5.96% |
| 11 | 9 | 0.17% |
| 12 | 49 | 0.92% |
| 13 | 289 | 5.45% |
| 14 | 238 | 4.49% |
| 15 | 55 | 1.04% |
| 16 | 170 | 3.21% |
| 17 | 373 | 7.03% |
| 18 | 33 | 0.62% |
| 19 | 92 | 1.73% |
| 20 | 368 | 6.94% |
| 21 | 234 | 4.41% |
| 22 | 64 | 1.21% |
| 23 | 218 | 4.11% |
| 24 | 347 | 6.54% |
| 25 | 47 | 0.89% |
| 26 | 103 | 1.94% |
| 27 | 282 | 5.32% |
| 28 | 159 | 3.00% |
| 29 | 52 | 0.98% |
| 30 | 127 | 2.39% |
| 31 | 242 | 4.56% |
| 32 | 23 | 0.43% |
| 33 | 57 | 1.07% |
| 34 | 164 | 3.09% |
| 35 | 76 | 1.43% |
| 36 | 27 | 0.51% |
| 37 | 68 | 1.28% |
| 38 | 108 | 2.04% |
| 39 | 11 | 0.21% |
| 40 | 21 | 0.40% |
| 41 | 62 | 1.17% |
| 42 | 31 | 0.58% |
| 43 | 6 | 0.11% |
| 44 | 24 | 0.45% |
| 45 | 33 | 0.62% |
| 46 | 1 | 0.02% |
| 47 | 7 | 0.13% |
| 48 | 28 | 0.53% |
| 49 | 15 | 0.28% |
| 50 | 1 | 0.02% |
| 51 | 5 | 0.09% |
| 52 | 7 | 0.13% |
| 53 | 0 | 0.00% |
| 54 | 2 | 0.04% |
| 55 | 1 | 0.02% |
| 56 | 4 | 0.08% |
| 57 | 1 | 0.02% |
| 58 | 1 | 0.02% |
| 59 | 1 | 0.02% |
| Total | 5304 | 100.00% |
This question was raised and discussed in the forum of my companion site Wizard of Vegas.
| Dice Test Data | |
| Dice Total | Observations |
| 2 | 6 |
| 3 | 12 |
| 4 | 14 |
| 5 | 18 |
| 6 | 23 |
| 7 | 50 |
| 8 | 36 |
| 9 | 37 |
| 10 | 27 |
| 11 | 14 |
| 12 | 7 |
| Total | 244 |
The chi-squared test is perfectly suited to this kind of question. To use the test, take (a-e)2/e for each category, where a is the actual outcome, and e is the expected outcome. For example, the expected number of rolls totaling 2 in 244 throws is 244×(1/36) = 6.777778. If you don’t understand why the probability of rolling a 2 is 1/36, then please read my page on dice probability basics. For the chi-squared value for a total of 2, a=6 and e=6.777778, so (a-e)2/e = (6-6.777778)2/6.777778 = 0.089253802.
Chi-Squared Results
| Dice Total | Observations | Expected | Chi-Squared |
| 2 | 6 | 6.777778 | 0.089253 |
| 3 | 12 | 13.555556 | 0.178506 |
| 4 | 14 | 20.333333 | 1.972678 |
| 5 | 18 | 27.111111 | 3.061931 |
| 6 | 23 | 33.888889 | 3.498725 |
| 7 | 50 | 40.666667 | 2.142077 |
| 8 | 36 | 33.888889 | 0.131512 |
| 9 | 37 | 27.111111 | 3.607013 |
| 10 | 27 | 20.333333 | 2.185792 |
| 11 | 14 | 13.555556 | 0.014572 |
| 12 | 7 | 6.777778 | 0.007286 |
| Total | 244 | 244 | 16.889344 |
Then take the sum of the chi-squared column. In this example, the sum is 16.889344. That is called the chi-squared statistic. The number of "degrees of freedom" is one less than the number of categories in the data, in this case 11-1=10. Finally, either look up a chi-squared statistic of 10.52 and 10 degrees of freedom in a statistics table, or use the formula =chidist(16.889344,10) in Excel. Either will give you a result of 7.7%. That means that the probability fair dice would produce results this skewed or more is 7.7%. The bottom line is while these results are more skewed than would be expected, they are not skewed enough to raise any eyebrows. If you continue this test, I would suggest collecting the individual outcome of each die, rather than the sum. It should also be noted that the chi-squared test is not appropriate if the expected number of outcomes of a category is low. A minimum expectation of 5 is a figure commonly bandied about.
Not that you asked, but the probability of one pair is 16×combin(15,2)×22/combin(32,4)=18.69%.
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Pictures taken from my companion site Wizard of Vegas.
This question was raised and discussed in the forum of my companion site Wizard of Vegas.

