I will be providing weekly score projection from here on out for both the Alpha and Zeta conference.
I received this from a user named Satchmo-n-Dizzy.
Here is how it works and information that it can provided.
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The reports available (all are available upon entry of game 1 scores):
1) Current Standings.
2) Projected Standings. (through game 16)
3) Toughest Remaining Schedule. (ranks conference by difficulty of remaining sched)
4) Easiest Remaining Schedule.
5) Toughest Schedule So Far. (ranks conference by difficulty of sched played to date)
6) Easiest Schedule So Far.
7) Toughest Opponents in Defeats. (ranks conference by difficulty of opponents that have beaten a given team)
8) Easiest Opponents in Defeats.
9) Toughest Opponents in Victories. (ranks conference by difficulty of opponents that have been beaten by a given team)
10) Easiest Opponents in Victories.
11) Points Scored. (most -> least)
12) Projected Points Scored. (over 16 games)
13) Points Allowed. (least -> most)
14) Projected Points Allowed. (over 16 games)
What is the algorithm?
The algorithm is actually very simple. It uses a comparative analysis of scores across all games to arrive at factors for each team (expressed as a %). The analysis will easily determine the SoS that a team has played to date vs what said team will play over the remainder of the season. It becomes more and more accurate as the season wears on. However, it has no ability to recognize or account for significant changes to rosters (gutting, recruiting) that may occur during the season.
There is far too little data to arrive at a consensus for week 16 games against the other conference, so it is not unusual to see the algorithm arrive at considerably different conclusions based on the perspective. But within a given conference, the results has been impressive thus far.
The concept...
A team 'A' that averages 56 PF and 6 PA is to play a team 'B' that averages 21 PF and 4 PA. How does one determine the better team going in? By looking at those numbers relative to the teams played to date. Team A's average opponent scores 36 ppg while allowing 28. Team B's average opponent scores 24 and allows 7. That means that Team A scores 200% of the norm allowed against its opponents while allowing 17%. Team B also scores 300% but allows only 14%.
Simplified, the algorithm would take Team A's 200% and multiply it by the 4 PA by Team B (8), then take the 17% and multiply it by 21 (3.5). For Team B it would take 300% and multipy it by 6 (18), then the 14% multipled by 56 (8). The algorithm would then resolve these numbers by taking the mean of each result = Team A will score... (8 + 8) / 2 = 8. Team B will score... (3.5 + 18) / 2 = 11.75. The projection picks Team B.
In practice, the algorithm will look at each game played by a team independently. So upward (or downward) trends will not be tempered by league averages. The determined percentage is, in essence, an average. And, of course, the projected score is and average as described above. However, the numbers that drive the analysis are much more volitile. This allows for greater accuracy based on trends and will typically gain in strength during the season.
I received this from a user named Satchmo-n-Dizzy.
Here is how it works and information that it can provided.
----------------------
The reports available (all are available upon entry of game 1 scores):
1) Current Standings.
2) Projected Standings. (through game 16)
3) Toughest Remaining Schedule. (ranks conference by difficulty of remaining sched)
4) Easiest Remaining Schedule.
5) Toughest Schedule So Far. (ranks conference by difficulty of sched played to date)
6) Easiest Schedule So Far.
7) Toughest Opponents in Defeats. (ranks conference by difficulty of opponents that have beaten a given team)
8) Easiest Opponents in Defeats.
9) Toughest Opponents in Victories. (ranks conference by difficulty of opponents that have been beaten by a given team)
10) Easiest Opponents in Victories.
11) Points Scored. (most -> least)
12) Projected Points Scored. (over 16 games)
13) Points Allowed. (least -> most)
14) Projected Points Allowed. (over 16 games)
What is the algorithm?
The algorithm is actually very simple. It uses a comparative analysis of scores across all games to arrive at factors for each team (expressed as a %). The analysis will easily determine the SoS that a team has played to date vs what said team will play over the remainder of the season. It becomes more and more accurate as the season wears on. However, it has no ability to recognize or account for significant changes to rosters (gutting, recruiting) that may occur during the season.
There is far too little data to arrive at a consensus for week 16 games against the other conference, so it is not unusual to see the algorithm arrive at considerably different conclusions based on the perspective. But within a given conference, the results has been impressive thus far.
The concept...
A team 'A' that averages 56 PF and 6 PA is to play a team 'B' that averages 21 PF and 4 PA. How does one determine the better team going in? By looking at those numbers relative to the teams played to date. Team A's average opponent scores 36 ppg while allowing 28. Team B's average opponent scores 24 and allows 7. That means that Team A scores 200% of the norm allowed against its opponents while allowing 17%. Team B also scores 300% but allows only 14%.
Simplified, the algorithm would take Team A's 200% and multiply it by the 4 PA by Team B (8), then take the 17% and multiply it by 21 (3.5). For Team B it would take 300% and multipy it by 6 (18), then the 14% multipled by 56 (8). The algorithm would then resolve these numbers by taking the mean of each result = Team A will score... (8 + 8) / 2 = 8. Team B will score... (3.5 + 18) / 2 = 11.75. The projection picks Team B.
In practice, the algorithm will look at each game played by a team independently. So upward (or downward) trends will not be tempered by league averages. The determined percentage is, in essence, an average. And, of course, the projected score is and average as described above. However, the numbers that drive the analysis are much more volitile. This allows for greater accuracy based on trends and will typically gain in strength during the season.
