In an effort to create the most reliable ranking system for college lacrosse, Lax.com has just introduced code that grades each team by strength of schedule, ratings percentage index, the USILA Coach's Poll, and a Lax.com “power rank” that is computed by weighting game results against several variables (inclusive of the prior three components above) and coefficients.

You can see the ratings for the three divisions here: D1  D2   D3

The above links are also now permanently on each division's scores page.

We know there are other opinion polls, rankings, and algorithms out there already. But what makes Lax.com's system unique is that we're going to tell you exactly how we calculate it, and we want your feedback and suggestions on how to make it even more accurate. In short, we're open-sourcing our rankings.

We'll be taking your suggestions (please leave in the comments of this story, which will have a permanent link from the ratings pages) and refining the code over the off-season with any of the ideas that will likely improve it. We'll then be posting various versions of the differing algorithms and be asking for your input as to which methods and weightings make for the most accurate solution. So, below, is how it works for now:

RPI (25%) - shorthand for Ratings Percentage Index, this is a pretty standard metric used by the BCS and NCAA basketball. It's comprised of a team's winning percentage (25%), the team's opponents' winning percentage (50%), and the team's opponents' opponents' winning percentage (25%). Note that only games inside of the team's division are counted, and games against the team for which the percentage is being calculated are excluded (ie, in calculating a Syracuse opponent's winning percentage the game against Syracuse itself is not counted). The max value for the RPI is 1.000

SOS (25%) - Strength of schedule. Another standard metric which is 66.6% a team's opponents' winning percentage and 33.3% a team's opponents' opponents' winning percentage. The max value for the SOS is 1.000

USILA Coaches Poll (25%) - We take the total points each team has received in that week's poll and normalize the numbers so that 1.000 is the max. For example, in the most recent poll Syracuse came in #1 with 213 points, while Villanova came in at #10 with 118 points. So we just divide every vote total by 213, giving Cuse a score of 1.000 and Nova 0.554. Doing it based on total votes, as opposed to the poll rank that USILA generates from those votes, makes the rank much more accurate. If there were 5 teams bunched up at the top that only differed by maybe 10 total votes, their rank should reflect that proximity. Otherwise we'd have to do it on discrete stepwise points system where #5 had a score 20% lower than #1, when that #5 team could very well only be a smattering of total votes behind the #1 team.

Lax.com PowerRank (25%) - This is calculated in two stages as a recursion is needed to generate the score. As with the USILA poll above, the end result is normalized so that 1.000 is the max score.

Part 1: We cycle through a team's schedule, excluding any games that were outside of their division, and create an initial (temporary) PowerRank to be used as the basis for the calculation in Part 2, each points result below being summed for the team in question as we go through all their games. If you lost the game you're awarded 0 points. If you won then the following points are awarded (as a sum of each line below):

- the opponent's RPI score * 0.75 (our RPI coefficient)
- the opponent's normalized Poll score * 1.0 (our Poll coefficient)
- 1.5 (the fixed var for winning a game) * the opponent's SOS score
- the margin of victory * 0.04 (our goal differential coefficient)
- if win was home we add -0.1, if away we add 0.1 (our fixed var for home advantage)

After summing the above for every game on a team's schedule, we then divide by the number of games played, and normalize this result so the top score is again 1.000. This result represents the initial PowerRank score for that team.

Part 2: We now cycle through every game on a team's schedule again, this time with the initial (temporary) PowerRank number available for each opponent. If you won the game then you get 4 * the opponent's PowerRank. If you lost the game, and your opponent's PowerRank is greater than 0.333, then you get 0 points. But if you lost to a team with a PowerRank <= 0.333 then we subtract the amount of the difference between 0.500 and PowerRank of the team to which you lost.

We sum these points for every game then, as we do in Part 1, divide by the number of games played and normalize that result so 1.000 is the max. The resulting number represents the real, final PowerRank.

We look forward to your feedback.