[Kicker Rankings below, and DST post here] Hi everyone! I don't remember when I last made an early-week version of Kickers. Mostly I've done Thursday aggregate rankings in 2024.

But today I wanted to draw your attention to a new Kicker tool:

Background

I've had this creation in the works for a long time. In a way, it's the culmination of many various developments:

• Last week, I posted my most complete (and hopefully clearest) explanation of all the considerations that go into the predictive modeling. Read it if you want to know the whole philosophy.
• And back in week 3, I'd made a post explaining what variables determine likely field position for kicking. Answering "which variables determine long kicks versus short kicks?"

I think BOTH of these posts^^ provide some of the most super-interesting stuff to explore. So I hope you didn't miss them. (If you like the nerdier side of this stuff.)

Situational Kicker Curves

And ^^these two pieces of background bring us to this new visualization: The whole purpose is to better communicate "What kind of situation the kicker is likely to see-- relative to the average kicker circumstance?"

If you go to the website front page now, you can freely browse images like the following for all kickers (The top 8 are free on the front page):

These new charts appear on the tool-tip, for all kickers. It shows how much more likely a kicker is to be at each field position.

I programmed the text explanations in the bullet-points, so they are auto-generated, to help you interpret the graph.

The example shown above is curvier than some others. Here's how to read it, starting from left to right:

1. Punts. At the far left, the curve goes negative, which means this team is expected to punt less often than other teams. All these are for the current match. Not necessarily for all games in the future.
2. Fourth downs. Next-- at about 55 yards-- there is a region labeled "4th", which means the models believe that this kicker's team will go-for-it-on-the-4th (not punt, and not take a long FG), at this section of the field. The width of 4th down region is different for each kicker-- and for each matchup.
3. Long Field Goals. Then we come to FG range, and you can see that the curve bends to the positive. The models think this kicker will have slightly more chances to take long kicks. (Compared to the average kicker.)
4. Short field goals. Interestingly, for this example, the curve goes negative again. That means the team is less likely to stop in the region of a 10-29 yard field goal, compared to the average kicker. (That doesn't mean it won't happen-- in fact there's still a good chance of ending up there-- it just means it's more likely to happen for other kickers than this one.)
5. RZ. In this case it's weakly relevant due to point #4. But again, the "4th" label means this team is expected more likely to go-for-it-on-the-4th (not take the FG), in cases when they end up a few yards shy of the end-zone.
6. TDs and PATs. Finally, to the far right, the curve bends up in the end-zone, meaning this kicker should see slightly more PATs than the average. (The occurrence of two point conversions is considered here too, by the way.)

Background: Input data

At risk of repeating myself... Very many pieces go into all this, even though it comes out like a simplified picture. So a reminder, here are examples of some variables that I test for statistical significance (original post here):

• TD- related: Extra points, Missed XPs, Two-pt conversions.
• FG-related: Missed FGs, Overall volume, and by distances: 20yd FG, 30yd, 40yd, 50+.
• Vegas lines: Own team implied score, and opponent implied score.
• Conditions: Wind, Grass/turf, Dome/outdoors, Rain, Snow, Home/away, Mondays, Thursdays.
• Team defense: rushing yards allowed, interceptions, early game points allowed, forced fumbles
• Team offense: Pace of game, Passing efficiency, QB rushing, 1st-quarter scoring, RZ
• Opponent: Division, Playoff chances, Drive success rate, allowed rushing yards, RZ%-allowed scoring.
• A lot of other: rushing or receiving volumes and efficiencies, sacks, turnovers, etc.
• And yes: Fourth downs.

What about "Why so high / why so low?"

The new Situational Kicker Curve tool above is something calculated from primary inputs. In contrast, the "Why so high / why so low" tool usually identifies those primary inputs. The inputs feed into making that the curve. (And, of course, the resulting curve helps to describe the expected fantasy points). So the two tools are not redundant-- they work together.

Kickers for Week 10

To celebrate this milestone-- meaning a tool that I will personally enjoy even if nobody else does!-- I will break from tradition of not ordering kickers here on Reddit. So for this week, here is a list of the current (Tuesday) rankings:

I hope you can tell, I've always really like sharing things, so I'm happy to be able to share this before we add some restrictions to the site next week (some content to be locked-- Reasons due to my situation, which I posted at my private channel here). So hopefully you'll enjoy this as much as I will. I'll be interested in your feedback-- There are kinks to iron out, but overall I hope it helps your decision-making process. For me, that's what it's all about!

Good luck.

/Subvertadown