Last week, you were all witnesses to me losing my cherry. As awkward as it was to have you all there during my deflowering, I enjoyed the experience immensely. My initial article covered the basics of using a handful of Frozen Pool tools to dig up early season talent. Once November arrives, those gems are no longer flying under the radar. Your competitors have long uncovered them and have the edge on your team. It’s time to pan the waiver wire and concentrate on short-term flyers!
A couple of my tools haven’t been properly explained (my bad), so I thought I would expand on last week’s newbie guide and create an intermediate sequel. The Roto Tools aren’t as self-explanatory as Compare-A-Player, so this is a perfect time. Our first foray into the advanced tools will be the RotoRanker.
Some players are very strong in one category but defecate the bed in everything else. This might be fine for a throwaway PIM goon on your squad but there’s a reason why Chara, Perry, OV, Stamkos, Kesler & Staal are rotisserie darlings. In standard Yahoo Roto leagues, they are called 6-Tool (aka Multi-Cat) players.
What if there was a way to quantify all the player categories and list the top overall 6-Tool players?? Hmm if only something that fantastic existed… if only… ok fine – loaded question. Here’s the RotoRanker output for the Top 10 Rotisserie players of 2010-2011.
Each statistic for each skater is ranked. As you recall, Corey Perry won the Rocket Richard trophy. Ipso facto, he was first in goals (G). He was 11th in assists and so on. As you traverse across his statistical rankings, you come to the Score column. This is a composite score of all his rankings; in other words, the sum of all the ranks. A lower Score means a higher rank average. Make sense? I’ll pretend you said “Absolutely! Tell me more!”.
Not only can you look at the whole season but also you can select yesterday, last three days, week, two weeks etc… It’s quite a powerful tool and does a lot of the homework for you. If you’re only interested in players who are hot, have lots of PP and ice time, I added a simple ‘PP and Points’. It’ll show you Goals, Assists, Points, Power play points, % Team PPTOI, PP/TOI and total ice time. Bargain Bin is all the basics but also factors in Caphit and dollar per point.
As a side note, % Team PPTOI is a Frozen Pool exclusive statistic. What it represents is the percentage of the team’s PP time the player in question is on the ice for. If a team played a game with five minutes of power play time, and your player was on the ice for 4 minutes of it, that’s 80% of the team’s PP time (%PP henceforth). It’s a much more accurate way of gauging a player’s situational utilization. As of this moment, Ilya Kovalchuk has a 97.5 %PP. That means he’s pretty much on the ice for EVERY SECOND of Devil power plays. The next forward is Evgeni Malkin at 74.1%. What is interesting is that they both average 4:47 of PP time. But using Frozen Pool’s %PP stat, it gives a deeper insight. I’ve also translated this method to total (%TOI) and short handed ice time (%SH). Trust me, get used to this stat, it’s very powerful.
Not all leagues are the same. There’s probably an infinite amount of league scoring combinations. Lucky for you, I’m all over it. Right under the Fantasy Scoring Template, you’ll see Default and Custom. Hit Custom and you’ll be presented with an Add Stat button.
When you click on ‘Add Stat’, a pull down bar will show up. You then select the statistical category you wish to rank. Repeat this process for all your league stat categories. For this particular example, I’m only interested in Points, PP Points, %PP, %TOI and shots.
The output is as follows:
Exactly as you requested! Note #3, Kovalchuk is #1 ranked for %Team PP. Everything is falling into place…
Now it’s time for the more complex RotoRater. This is a much more advanced and math intensive ranking system. We ditch the simple 1st, 2nd, 3rd stuff and migrate to nerdy degrees of standard deviation.
Remember back when you were pulling in solid D average in high school? (and by you I mean me). You might recall the infamous bell curve. It’s pretty much a statistical density scale. Most of the grades are huddled in the middle, the intelligence-deficient kids are on the far left and the chess club members are on the far right.
All you really have to know about standard deviations is that they are the vertical lines within the bell curve. They are the difference of each data point from the average – being zero (or the middle). For the RotoRater, I rate players based on how far to the right or left their stats are. The more positive a number, the more dominant that player is leading that category. If he is sucking it up, you’ll see a negative. For instance, in 1990, Brett Hull was so far ahead in goals (86) than the next player, he’d have a crazy high Goal score. Why? Because he is many standard deviations away from the average. Here is a practical example for the top RotoRater players of 2011-2012.
There are a few quick items you can deduce from this table.
- Kessel is below league average in penalty minutes and handsomeness.
- Daniel Sedin has lots of PP points.
- Teemu is kicking ass in PIM. Wait, what? A 22 PIM game the other day helped that. Remember, my tools don’t lie; unlike those strippers who say they dig you.
- James Neal has poor +/- and PIM stats but he makes up for it in Shots.
What is more interesting is that I rate these players amongst their positional peers (Pos Score) and the rest of the NHL (NHL Score). Ever wonder why left wingers go fast in drafts? It’s because elite LWs aren’t as abundant as Cs or RWs. What I’ve successfully done is quantified this practical bias for you!
The same logic applies for the positional score. Rather than comparing the student to the entire school (NHL Score), he’s ranked among his fellow grade 10s (Pos Score). Instead of grades, it’s C,L,R,D. By the way, if you wish to sort by positional score, just click on the ‘Pos Score’ table header.
So there you have it. I hope the past couple weeks have been helpful in explaining the ranking tools. Have fun!
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