10 thoughts on “When bar charts attack

  1. As you said – t’is easy to play well when your opponents have a 2 goal cushion. Thus the frequency of 1-2 (i.e. home side pushes hard and nicks a goal) and 1-3 (i.e. the home side pushes just a little too hard for an equaliser and the away side beats them on the counter).

    Still think we played impressively well yesterday though.

  2. Trend and outcome were unremarkable, as implied by the bar-chart and ImperialWhite’s comment.

    Exclusively notable were the extent, duration and relentless of the turnaround – a close runner-up to which I struggle to recall from zillions of Fulham matches seen. I struggle to recall most things of course.

  3. I guess that graph refutes every commentator’s go to line when a team scores a second goal ‘2-0 is the most dangerous scoreline’ – It quite obviously is not.

    1. Well, on the other hand, the team up 2-0 seems to concede at least one goal much more often than they maintain the 2-0 lead.

      1. that’s what I mean, momentum does shift, so the saying’s kind of right, but it’s surprising how infrequently we see a full comeback. Or perhaps it isn’t surprising really.

  4. P.S. Where did you get that data? I’m assuming you didn’t compile it yourself (kudos if you did!).

    2-0 might not be a dangerous scoreline, but I bet 0-1 at HT *IS* a dangerous scoreline (am thinking along the same BetFair lines as you!).

  5. The data gives you pre-match odds, which I’ve used a number of times before here, and which I’ll use this week to investigate “the etuhu effect”. But in the meantime, it would be interesting to build a database so you can filter by HT score cut by type of game, e.g. if the strong pre-match favourite is 1-0 up at half-time, what is likely for the second half? I think doing this at HT may be more powerful than pre-match because it takes things like team selection, teams being ‘up for it’ out, which you wuoldn’t be able to tell before the game.

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