Timothy Shannehan: PRO View

NCAA Transfer Rankings - LacrosseReference

Player Comparison

To compare players based on my opponent and usage-adjusted rating system, search for their names, select the players from the list, then click `Get Data` to see their numbers.

Saved Comparisons

How This Works

This page uses player ratings that are calculated to account for two factors: 1) the strength of the opposing defenses faced and 2) each player's usage rate.

Step 1 is to calculate the skill-specific Madden percentiles for each player.

These ratings are not adjusted for opponent or usage-rate, but they do improve on basic counting stats. As an example, turnovers-per-game is a tough metric to rely on because the players with the ball in their stick the most tend to have the most turnovers. A turnover rate that takes turnovers and adjusts for touches is going to give you a better measure of "does this player have a ball security problem?" than raw turnovers-per-game will.

They put every player's performance on a 0-100 scale so that you can easily identify strengths and weaknesses. That also makes them the perfect input to a opponent-adjusted player rating system. Step 2 takes the Madden percentiles and evaluates them with the extra context I mentioned above: opponents faced and usage rate.

I'll explain the opponent-adjustment first, because that's probably the most intuitive: if you have two players with similar stats, but one played in a top-3 conference and one played in a bottom-3 conference, you know that the player who played the tougher schedule is actually the better player. Take this real-world example.

TO/gm

Play Share

Opp Strength

Ball Sec.

Player A

1.4

1st

+0.4%

Player B

1.8

1st

+2.3%

Both players were the top option in their offense, and after accounting for their usage rates, they committed turnovers at basically the same rate. In other words, their non-opponent-adjusted ball security ratings were the same. But Player B faced defenses that forced turnovers at higher clip than those that faced Player A. (The +2.3% means that Player B's opponents forced turnovers at a rate 2.3 percentage points higher than th national average.)

When you account for that, Player B's ball security rating comes in 8 points higher than Player A's. Their individual rate stats were the same, but we can use the opponent adjustments to quantify the gap between the two of them.

I won't give you another table, but we can do the same thing with usage rate as the differentiating factor. Two players on the same team are always going to face the same defenses, but their respective roles in the offense mean that's not really true. If you are a 4th attackman, then the opposing defenses probably aren't going to have their best cover defenders marking you. That should give your numbers a boost. Using usage-rates to account for this means that, even within a single roster, we can convert the basic rate stats into something that gets closer to the reality of each player's skill level.

Step 3 is simpler. We take all four of the individiual ratings for each player and average them. That gives us a 0-100 overall rating for every player that accounts for both their role in their offense AND the quality of the defenses faced.

To put this in context, here are the first two big offensive names added to the DI MLAX transfer portal in 2026.

Finishing

Passing

Ball Security

Ground BallsGBs

Overall

Garrett Haas

Timmy Shannehan

Both guys are about equal in terms of their ability to win ground balls. Haas is the better finisher, but Shannehan grades out as the better facilitator. Overall, if you are just thinking in terms of overall impact, you'd give the slight edge to Shannehan.

Extra Bits

One thing that's cool about the opponent adjustments described above is that they are sensitive to which games each player recorded their stats. If you recorded most of your stats in garbage time when your team was up big against overmatched opponents, then your weighted opponent adjustment will be different than a teammate who played equally in all games.
DIII-to-DI transfers always present a challenge. You have the eye-test, but you know that those players did not face the caliber of opponents that they would face in DI. We don't have a ton of data, but it's enough to start to get a sense for how a DIII-to-DI move "should" affect a player's adjusted player ratings. And that's useful; even if we apply a standard average adjustment, which is directionally correct, it's a more credible way to forecast how a particular statistical profile will hold up across that transition.
EGA is referenced above. It stands for Expected-Goals-Added. It's like WAR in baseball: an all-in number that quantifies all the good and all the bad things a player does on the field. For more definitions, check out the glossary.

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