# Statistical Predictions

Has anyone run predictive statistical models (a la Nate Silver) to predict later season competitions based on earlier results successfully?

I attempted to build a simple model that predicted Curie based on summing for each alliance each team’s best Event OPR from earlier in the season. It scored 21/81 based on Thursday’s matches. You’d be better off taking the opposite of my model by a long shot!

Team age seems to be doing a little better than a coin flip - 44 / 79 (ignoring the tie in match 39).

Looking at the summary statistics of match score differences, Curie has the closest of matches:

``````
Correct	Q1	Q2	Q3	AVE	STDEV
Archimedes	51	39	63	89.25	70.3	51.1
Curie		44	19	41.5	74.5	61.7	58.8
Galileo		51	29	55	101.25	71.9	55.8
Newton		49	25.75	47	102.5	66.3	53.9

``````

(quartile, average, and standard deviation taken from the difference of match scores).

I’ve been doing it for several years, and this year it’s been the worst by far. The game could hardly be more interdependent, which makes any OPR-based predictions bunk. Also, I should note that I’ve generally found Most Recent OPR to be a better predictor than Max OPR.

Basel gets at the core of the issue for this year’s game. The independence assumption inherent in OPR-like regressions doesn’t model a game where a plurality of points scored are from cooperation between alliance members.
Unfortunately the data from matches is too sparse to model interactions between specific teams, so we would have to use more advanced techniques (e.g. clustering teams into equivalence classes) which brings an additional set of approximations.

Won’t go into too much detail now because I’m on my phone, will return later. but I’ve successfully predicted ~78% of matches up until about 2 today for all fields at champs. A sizable percentage of the matches it gets wrong are close matches.

Essentially I score each team by what the average score is during a match they are in as opposed to when they aren’t. Maybe I’ll do a small white paper on it to explain further. But nonetheless I will be back this evening if people are curious.

Why do you suppose this should give a better result than an OPR, CCWM, or EPA calculation?

What does OPR, CCWM or EPA yield for percentages?

Hi Ether,

Going to start this off with an anecdotal statement. We ran these same statistics for last season and compared them to OPR. Last season, OPR was a little better at predicting matches than what we are calling Main Effects. So I cannot claim that Main Effects always gives better results than OPR or the rest.

As I said, we calculate ME by taking the averages of every match a team is in, and compare them to the averages of matches they aren’t in. We do this for each piece of the match we can get from Twitter, so Auton, Tele, and Foul points. With that we get three numbers that tell us how good or bad a team is compared to the average team. A ME # of 0 in Auton means that their auton average is equal to the average auton for every alliance this season. We sum up those 3 numbers to get a Final ME on the entire match.

Because of the nature of this game and how much defense and fouls come into play, we do the same thing, but look at the opposing alliances scores. So when team X is on an alliance, how does the opposing alliance score? This is incredibly telling for foul points and can pinpoint a robot that has been in a lot of foul matches very easily. If a team is incredibly good at defense (or so good at offense that its presence forces the other alliance to dedicate robots to defense), you will see the Opposing Final ME be a negative number.

For match predictions then, I tally up the Red Alliance’s “Score” as so. I sum up the 3 Final ME of the teams on the red alliance with the 3 Opposing Final ME of the teams on the blue alliance. I do the same for blue and use those numbers to predict the outcome of the match.

So just to display an example team ME spread, here is 1114 from this year:

``````
TEAM	MATCHES	FINAL	HYBRID	TELE	FOUL	OPPSING_FINAL	OPPOSING_HYBRID	OPPOSING_TELE	OPPOSING_FOUL
1114	38	102	19	71	13	-2	1	-3	2

``````

1114 scores, on average, 102 points higher than the average alliance. This is largely contributed to their tele ME of 71. We can also see that alliances that play against them average a score of 2 less than the average alliance score.

Why do I think this has so much success this year where OPR hasn’t? I think it can be largely attributed to the effect fouls have had on the game, as well as the fact that a good alliance consists of more than 1 good robot this year. I personally believe that that fact is what makes this game so good. Others clearly have opposing opinions, but the fact that one robot can’t drive an alliance to victory single-handedly supports FIRST’s mission far greater than everyone sitting behind while one team drags them to victory.

There are most likely many flaws in my reasoning that I’m willing to discuss because I honestly do not believe I have the definitive answer as to why it has been successful. But I would like to find out.

For everyone else who is paying attention, after every match today, ME has successfully predicted 465 out of 600 matches, or a 77.5% accuracy at championships using regional data. This coincides with my results throughout the season landing between 70% and 80% accuracy at each regional after the fact. Seems that that last 20% is most likely too random (robots breaking) to achieve.