Actual goals scored this year is 340 over 125 games, which is 2.72 G/Gm, which is 1.36 G/Gm/Team, which is pretty close to 1.3. Also xG does not account for own goals, which probably further reduces the gap if you also excluded those from the actual goal count.Interesting at the huge amount of teams right around 1.3/1.3. Is that the nature of the meta of MLS soccer?
Talles, to me, feels like he could have a potential Taty explosion a la last year. Taty had great xG numbers but wasn't finishing until his mother visited, then he went on a tear to end up matching/coming close to his xG output and getting the golden boot.Which graph do you like best?
The one naming Keaton Parks as the top center mid?
Or the one showing that we have 3 of the top 11 wingers in the league?
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And Oh My GOD! Talles Magno! My eyes… they BURN!
Getting that one in early, eh? Good move, total thumbs up! LOLOnly then can he truly become the TALLEST MAGNO
I was actually going to look into this later today as somebody on Twitter prompted me for it.I'm trying to keep track of NYCFC's "home" record at various locations in all competitions and would appreciate it if anyone wants to check my work so far.
I am not counting 2 MLS-Is-Back games in Orlando that were artificially designated home games. Other than that, am I missing anything, wrong results, wrong counts?
Pre-2017: Two USOC games at Hofstra and at Fordham, both losses. Cume 0-2-0
2017 Hartford and Citi. Both MLS games both draws. Cume 0-2-2
2018 none
2019 Belson USOC win Cume 1-2-2
2019 Citi Playoff loss Cume 1-3-2
2020 MLS RBA 4-1-0 Cume 5-4-2
2020 CCL RBA 1-1-0 Cume 6-5-2
2021 RBA MLS 4-2-2 Cume 10-7-4
2022 Banc of Cali CCL win Cume 11-7-4
2022 Hartford CCL win Cume 12-7-4
2022 RBA CCL draw Cume 12-7-5
2022 Citi MLS 1-0-1 Cume 13-7-6
2022 Belson USOC win Cume 14-7-6
Seemed the best way to do this was to just snip an image of the excel I put together.I'm trying to keep track of NYCFC's "home" record at various locations in all competitions and would appreciate it if anyone wants to check my work so far.
I am not counting 2 MLS-Is-Back games in Orlando that were artificially designated home games. Other than that, am I missing anything, wrong results, wrong counts?
Pre-2017: Two USOC games at Hofstra and at Fordham, both losses. Cume 0-2-0
2017 Hartford and Citi. Both MLS games both draws. Cume 0-2-2
2018 none
2019 Belson USOC win Cume 1-2-2
2019 Citi Playoff loss Cume 1-3-2
2020 MLS RBA 4-1-0 Cume 5-4-2
2020 CCL RBA 1-1-0 Cume 6-5-2
2021 RBA MLS 4-2-2 Cume 10-7-4
2022 Banc of Cali CCL win Cume 11-7-4
2022 Hartford CCL win Cume 12-7-4
2022 RBA CCL draw Cume 12-7-5
2022 Citi MLS 1-0-1 Cume 13-7-6
2022 Belson USOC win Cume 14-7-6
As a general rule I also hate to count individual legs as stand-alone wins or losses while ignoring the context. The Columbus game you mention is a perfect example. But I don't see a better way to handle it for this exercise unless you just ignore 2-leg contests. It is also ridiculous to count the Columbus game as a home loss, because (1) we really lost the contest in Columbus, and (2) it makes no sense to burden some games with handicaps when comparing venues.ETA: I'm not sure counting specific legs of a two game home/away series as a win or a loss is appropriate, but for this exercise I did so. The "draw" against Seattle above at RBA maybe should be counted as a loss and the one of the playoff wins at YS perhaps should be counted as a loss as well (return leg against Columbus). Especially considering they were second legs, but again, I'm keeping it as is.
And then obviously only calculated points and ppg for MLS Regular Season. Didn't feel like it was appropriate to add other competitions to those calcs.
Why not do what the Olympics does and throw out the high and low scores, in what mathematicians call a "sensibilized average" (LOL, just made that up, although I'm sure there's an actual name for that sort of thing). Should only be about three weeks' worth of research and calculation after which we can all go, "hmmm, not sure that made all that much difference." (also LOL).Here is another table I worked on. I left it out because I don't think it tells us very much. I computed the Average Margin of Winning and Losing every year. Then I compared with a simple subtraction. Before I collected the data, I thought that a positive number in the last column could be evidence that a team is "more efficient" when winning than its opponents. But I don't think that really is what this shows.
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I think this mostly shows that a good team will occasionally blow out its opponents more often than it gets blown out. I think 2015 just shows that a bad team with David Villa will sometimes win big while often being just bad enough to lose. These numbers are also based on ridiculously small sample sizes. The most wins NYC ever has was 18. After 2015, the most losses is 11. That means a single outlier really skews things. To take a famous example, if you exclude the RBW from the data in 2016, the average margin of loss goes down from 2.20 to 1.67, and flips the third column figure over 33 games to negative, like every other year but 2019.
I tried looking at this as another way of analyzing the issue of how the team tends to score in bunches under Ronny, or not score so much at all. But I don't think it helps. This post is just sort of a BTS look at how I sometimes look at something and then decide it's not actually meaningful.
But these are not 34 game samples. It’s separate samples of maybe 16 and 11.Why not do what the Olympics does and throw out the high and low scores, in what mathematicians call a "sensibilized average" (LOL, just made that up, although I'm sure there's an actual name for that sort of thing). Should only be about three weeks' worth of research and calculation after which we can all go, "hmmm, not sure that made all that much difference." (also LOL).
Would be interesting if it's 15 minutes work but certainly not worth a week of statistics mining, of course. In all seriousness though, I think 34 games is a large enough sample to toss out the two highest and two lowest goal differentials, say, to see if clipping out those outliers could yield some interesting and relevant info. And again, not at all trying to assign homework here!
And love that game in hand for a bit of insurance.It feels like it's been a rough follow-up to our championship season, but after 16/17 games including tonight's draw, Philly's loss, Montreal's win over Seattle:
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