This is the most sensible post I've read on here for a long timeWe should have kept Benjani
This is the most sensible post I've read on here for a long timeWe should have kept Benjani
You may end up being correct but at the moment there are few signs that anyone is going to run away with it and the main players could be classed as being shit again (points wise). If it took 90+ points again to win it, i am with you, I don’t think Spurs get there. If it’s 80ish points, I think they will be in the mix.And everyone else was shit that season. No way Spurs will come close to the title.
we said the same about Leicester 5 years ago.Eventually Jose’s defend, hoof, and score on the break tactics every game will be found out. Especially if Son and/or Kane get injured.
I can’t wait for these stats jokes.That’s a fair point, though, as we were discussing who was the better player, I was providing per 90 stats from last season (the most recent complete data universe) That is because for most absolute determinations relevant to a current or near-future period (assessment of current preference or superiority), recency is king (i.e. you need a sufficient universe, but the more recent the data in that universe, the more accurate the forecasting likely is).
After a certain point (which one has to define in context of the system in which the evaluation is taking place), the further you go back in time, the less relevant the data for current assessments. It works the other way, as well: if you limit your universe to only very recent data, you tend to only capture transient states, rather than likely persistent ones.
Defining a relevant, representative, statistical significant universe is half the battle in data analytics, modelling, and forecasting.
All of that said, I didn’t see their historical conversion rates in the links you posted — could you focus my view on them?
Genuinely interested to look at them.
Beautifully written.That’s a fair point, though, as we were discussing who was the better player, I was providing per 90 stats from last season (the most recent complete data universe) That is because for most absolute determinations relevant to a current or near-future period (assessment of current preference or superiority), recency is king (i.e. you need a sufficient universe, but the more recent the data in that universe, the more accurate the forecasting likely is).
After a certain point (which one has to define in context of the system in which the evaluation is taking place), the further you go back in time, the less relevant the data for current assessments. It works the other way, as well: if you limit your universe to only very recent data, you tend to only capture transient states, rather than likely persistent ones.
Defining a relevant, representative, statistical significant universe is half the battle in data analytics, modelling, and forecasting.
All of that said, I didn’t see their historical conversion rates in the links you posted — could you focus my view on them?
Genuinely interested to look at them.