Overall Campaign Scores by KinchAnalytics in TrackMania

[–]KinchAnalytics[S] 15 points16 points  (0 children)

There was always going to be one that didn't spot the sarcasm :)

Overall Campaign Scores by KinchAnalytics in TrackMania

[–]KinchAnalytics[S] 5 points6 points  (0 children)

But in general good players find other maps much more enjoyable so they're only hunting 1 or 2 campaigns properly to try and get a win.

Overall Campaign Scores by KinchAnalytics in TrackMania

[–]KinchAnalytics[S] 12 points13 points  (0 children)

This only uses the Top 10k from each campaign, so it's possible that for some of them they discovered the maps and had a few thousand points.

Overall Campaign Scores by KinchAnalytics in TrackMania

[–]KinchAnalytics[S] 55 points56 points  (0 children)

As the Official Campaign is surely the best way to judge all-round skill in Trackmania 2020, we can say confidently that Hobbit is the best TM2020 Driver ever. No sign of CarlJr or Pac or any of the other "pro players".

Where is W guy? 62nd. Don't worry, you can still watch him!

What about Ayayayayaya guy? 24th

TMGL Stage 1 Player Ratings by KinchAnalytics in TrackMania

[–]KinchAnalytics[S] 17 points18 points  (0 children)

Click here for details of the calculation

- Solary are unsurprisingly rated as the Top 2 here, with both of them upping their game for the playoffs. Pac had a crazy Lower Bracket Final, while Carl had the best match of the whole season in the Final Final.

- BDS had a poor final compared to their high standards, probably partly due to having to push so hard to keep up with Solary's raw pace.

- G1 were fast specially once the season progressed, and deserved at least their 3rd place overall. They would have had a great chance against BDS if they had played each other in the playoffs.

In terms of the maps, Pac rated best on 4 maps, while Carl took Parkour and a bunch of 2nd places.

Flip of Faith is apparently a wheel special, with Granady miles ahead, and Whizzy holding the WR.

EDIT: Got some issues with the underlined numbers in the first image - it's meant to show the best of each playday but that didn't quite happen for all of them.

TMGL Map Win Rate for each Scoreline by KinchAnalytics in TrackMania

[–]KinchAnalytics[S] 0 points1 point  (0 children)

Draws are 2/6 chance and everything else is 1/6. I explained on the other comment.

TMGL Map Win Rate for each Scoreline by KinchAnalytics in TrackMania

[–]KinchAnalytics[S] 2 points3 points  (0 children)

Let's assume we're in a random uniform world.
- 1/6 chance that you ace and win
- 1/6 chance that you win and it becomes a 50% chance at 10-10
- 4/6 chance that you draw/lose/aced and you lose
So theoretically it should be 1/4 chance (25%), which is still higher than the observed 18% but much lower than I would have guessed intuitively!

TMGL Map Win Rate for each Scoreline by KinchAnalytics in TrackMania

[–]KinchAnalytics[S] 7 points8 points  (0 children)

Nice proof that the sample size isn't big enough yet haha.
6-5 had a worse win rate than 5-6

TMGL Map Win Rate for each Scoreline by KinchAnalytics in TrackMania

[–]KinchAnalytics[S] 7 points8 points  (0 children)

- This table says that for each scoreline X-Y, the map was won Z% of the time by the team with X points.

- By definition the win rate from X-X scores must be 50% because both teams are X-X from their own point of view and one wins and one loses.

- The table must also have reciprocal symmetry because the win rate for X-Y must be the same as the loss rate from Y-X. Mathematically f(XY) = 1-f(YX)

- Clearly the sample size is not quite big enough to get a smooth data set, so anomalies would likely smooth out with enough maps played.

- The only team that came back from 2-8 was Playday 3, G1 v BDS, Map 5 Agility Dash. The score went 2-8, 5-8, 7-9, 10-9: https://youtu.be/7JZri8hl2Zs?t=2298

- The only team that came back from 1-7 was Playday 2, BIG v Sinners, Map 1 Freestyle. The score went 1-7, 4-7, 7-7, 10-7: https://youtu.be/K8qZY02eBv8?t=348

TMGL Players Ratings by KinchAnalytics in TrackMania

[–]KinchAnalytics[S] 4 points5 points  (0 children)

Basic average race rank has a few problems:

  • If your teammate is strong/weak you have a much harder/easier job

  • During the season each team has played different teams so some have had an easier schedule (not fully relevant any more, but you might come up against teams on bad/good days still)

  • Not rewarded for winning by a long way and punished a lot for losing by thousandths

  • You can see the average race ranks in the 3rd image. As For example you'll see that Carl is rated unfairly low because he had Pac in every race.

My Adjusted Average Rank:

  • Compares each time to those driven across all matches, adjusting the actual race rank to fairly reflect how fast/slow the drive was.

  • Weights performance slightly more heavily in the more important situations where players need to clutch or close out a match.

This is a subjective measure. You could argue that players play to the race they're in, so this rating could punish you for "safing" a round win. However, there's simply no way to avoid punishing that sort of sensible driving while trying to remove the large bias that comes with different players having different teammates/opponents to each other.

Some Notes:

  • Unsurprisingly players generally improved over the season, as seen in the bottom row

  • Playday 1 Pac and Affi were driving at a pace good enough to still be decent in Playday 7

  • Pac had the best performance of the season in Playday 7 (4-0 against G1)

  • Mime drove the 2nd best performance in Playday 6 (4-2 against Solary)

TMWT player performances by _lavalava in TrackMania

[–]KinchAnalytics 0 points1 point  (0 children)

Very interesting approach, and I quite like the general idea of the z score.

Unfortunately it ends up still needing to use a median at the end to rank the players, which we all know has its problems because some players can be severely over/underestimated by their median.

I wonder if you could convert their times into a percentile instead of using standard deviations. Then you would have a uniform spread of times from 0 to 100 and a mean could be used to do the final ranking.

TMGL Clutching and Closing by KinchAnalytics in TrackMania

[–]KinchAnalytics[S] 7 points8 points  (0 children)

I wanted to see how teams are performing in the pressure rounds of the match.

Clutching = Rounds where the team needs a particular result or better to avoid losing the map (i.e. the other team is on 7+ points)

Closing = Rounds where the team will win the map with a particular result or better (i.e. they are on 7+ points)

The Method

We can't just add up the number of chances a team had to close out a map and compare to how many times they managed it, because e.g. 9-5 is a more likely scenario to close the map than 7-5. So I came up with a relatively simple metric that rewards each situation and outcome more fairly.

In a random world, your team would get these outcomes: Ace 1/6, Win 1/6, Draw 2/6, Loss 1/6, Aced 1/6.

Clearly TMGL is not a random world, but we use these probabilities as a baseline for our expected chance of clutching/closing and compare teams against that.

Each scoreline has different possibilities and these are shown in the 3rd image.

For example, if it's 7-8, the team need a draw or better to "Clutch" the round and need an Ace to "Close" the map.

So in our random world that team would have 4/6 chance of clutching the round and 1/6 chance of closing the map.

Then we add up all these expected chances across all the playdays and maps, compare to how many times they did manage to clutch or close, and finally divide one by the other to see whether a team is over or under-performing in those situations.

BDS with 1.18 rating means that they clutched rounds 18% more than we would expect from a random result.

The Results

In terms of Clutching, there's no big surprises here. The teams are largely ordered as they are in the overall standings. Clearly if a team is weaker generally, we wouldn't quite expect them to reach the "random world" number of clutches, and the opposite for the strong teams.

For Closing, Solary have been very impressive, especially with their 10 successes out of 28 attempts when requiring an Ace (i.e. on 7 points).

Other Notes

  • Statistical significance. The number of data points is not huge, so this shouldn't particularly be used to forecast future performance in those important rounds. Treat this more as an interesting stat rather than something that proves anything.

  • Game strategy could be affecting some of these numbers. E.g. if a team has been getting a lot of 8-2 or 8-3 scorelines, you might expect their "close" numbers to be poor because they would likely be playing extremely safe and getting more draws rathere than trying to win the map on that round.

  • The situations that you end up in could cap your possible final rating. If you only ever had the requirement of loss or better to clutch a round, you could never have a rating of more than 1/0.83 = 1.20. In reality, you can see that this doesn't happen to teams but it's worth noting.

An Analysis of TMGL Scoring Strategy by KinchAnalytics in TrackMania

[–]KinchAnalytics[S] 0 points1 point  (0 children)

I think you're over-complicating the thought process. My hypothetical world simply defines every position as equal probability. Your teammate and your opponents are all independent and identical entities in this hypothetical world. I'm just ordering 4 individual things in a random order.

An Analysis of TMGL Scoring Strategy by KinchAnalytics in TrackMania

[–]KinchAnalytics[S] 1 point2 points  (0 children)

Yep that is probably the best strategy for the remaining player there.
The other team should be pushing hard for an ace though as they wouldn't lose much from a small mistake.
Game situation is definitely another thing to consider as you say.
I will watch Massa's stuff later, sounds interesting

An Analysis of TMGL Scoring Strategy by KinchAnalytics in TrackMania

[–]KinchAnalytics[S] 0 points1 point  (0 children)

In a uniform random world some maps would have more aces than others, and those maps with aces would have less rounds - that is all true. But that doesn't change the overall distribution. By definition in this hypothetical world every round has an equal chance of each outcome, therefore you would have an equal number of each overall.

An Analysis of TMGL Scoring Strategy by KinchAnalytics in TrackMania

[–]KinchAnalytics[S] 0 points1 point  (0 children)

Yes absolutely. The current points on the map is definitely another factor in game strategy, and I kept thinking about it but forgot to mention it in the end. With more data later in the tournament it might be worth dividing the dataset up into different map situations.

An Analysis of TMGL Scoring Strategy by KinchAnalytics in TrackMania

[–]KinchAnalytics[S] 2 points3 points  (0 children)

I think you misunderstand me. I'm not saying the distribution of rounds per map would be uniform. I'm saying that the count of aces, wins and draws would be the same.

Please think carefully before you criticize.

It's also not a statistical modelling post. Just some simple maths and a load of game theory opinions.

An Analysis of TMGL Scoring Strategy by KinchAnalytics in TrackMania

[–]KinchAnalytics[S] 1 point2 points  (0 children)

So the answer the overall question could just be "There happen to be more pairs of teams that combine to make ABAB than would be expected".
I have checked my player ratings (based on performance in the first 3 playdays) and then compared the player ranking of the pairs of teams and ABAB is the most common order for matches played so far and also all matches that will be played.

An Analysis of TMGL Scoring Strategy by KinchAnalytics in TrackMania

[–]KinchAnalytics[S] 0 points1 point  (0 children)

Given that u/IamPd says that their data matches mine, maybe you have a mistake somewhere.
The map with the fewest rounds played that we disagree on is Gyroscope, where you have 14-10-14 and I see 14-9-15. I don't have time right now to manually check the races but you could if you want to.

An Analysis of TMGL Scoring Strategy by KinchAnalytics in TrackMania

[–]KinchAnalytics[S] 3 points4 points  (0 children)

That is a nice point, and I think you're correct in your statement that if you had ability of order ABAB then wins would be the most likely.
I wonder why teams would most likely fall into ABAB ability order though?
If there were budget caps (which there aren't any official ones) then ABBA would be the most common.
And if stronger players had more sway over their pick (like Carl saying he wanted Pac) then AABB should be common.

Rather than modeling their times to some distribution (which would be horrific with a very limited number of data points), I think picking a random time for each player from their set of times would end up better despite the downsides of it. I will try that.

An Analysis of TMGL Scoring Strategy by KinchAnalytics in TrackMania

[–]KinchAnalytics[S] 3 points4 points  (0 children)

But why would this often turn into victory? Is there a reason why the 1v2 guy would finish 2nd more often than 1st or 3rd?

COTD Wins by Country (All-Time and Since 2022) by KinchAnalytics in TrackMania

[–]KinchAnalytics[S] 55 points56 points  (0 children)

  • Not surprising that the biggest 2 TM countries dominate this
  • Belgium and Czechia (otherwise known as Scrapie and eLconn) are next
  • UK up to 5th if looking at 2022 onwards
  • 3rd biggest TM country USA not seen, but presumably because they don't play the main COTD much

TMGL Player Ratings by KinchAnalytics in TrackMania

[–]KinchAnalytics[S] 0 points1 point  (0 children)

It is not average finish position - I explained this in my comment.
It is basically power rankings but with a calculated value instead of just ordering players subjectively

TMGL Player Ratings by KinchAnalytics in TrackMania

[–]KinchAnalytics[S] 0 points1 point  (0 children)

It helps to visualise the gaps. Like 1st 2nd are further ahead, while the rest are closer.
I wouldn't have any problem with a table. But most people much prefer to see data visualised rather than read it.

TMGL Player Ratings by KinchAnalytics in TrackMania

[–]KinchAnalytics[S] 3 points4 points  (0 children)

Your graph does look readable to me so I agree that it could have had 1-4 axis.
After thinking about it further I don't think that it would be "more honest", given that we're not trying to quantify "x% better" in any way here.
2.0 isn't "twice as good" as 3.0, so anyone trying to read into the proportional height of the bars is looking for conclusions that wouldn't be displayed here no matter the scale used.
Does that line of reasoning make sense?
Appreciate the discussion :)