Influencing everything makes you influence everything too slow to have any effect - focusing on a few allows you to go faster, as the sum of influence spent in total remains constant. Apr 26, You get a base of 0.
This is modified by different modifier for each country. Such as relative score, neighbor, capitols on different continent, and discredit modifiers. Feb 13, 47 0. In my current game as SP the rate of increase of my "influence points" those used for GP actions is incredibly slow. In an earlier game it took no more than mins real time for my points to replenish making GP actions on neighbouring states a fairly active, and rewarding, process.
At my current rate of accrual I have not been able to initiate one single GP action in about 50 years of game time! I have no infamy and can use the diplomacy points those for non GP specific actions as they renew quite quickly. What affects the rate at which these influence points increase and how can I change it?
Click to expand I think I may have answered my own question - the rate of increase of influence points is based on things like prestige, military power etc and is applied to all countries evenly across the world. To increase influence points in relation to one country you have to use the priority bars to do so - increasing priority directs the influence points to that specific country or countries and makes GR actions more frequent.
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To start, we ranked the entire league based on win total. The Bucks ranked 1st with 60 wins while the Knicks finished last with If teams were tied for wins, like the Thunder and Celtics at 49, both received the same rank.
This ranking is the baseline to translate each statistic to a win. Then, using data from NBA. The categories used:. For example, the Lakers ranked 20th in wins, 24th in offensive rating and 13th in defensive rating.
The lower the differential, the better the category is at predicting wins. There is a caveat in this exercise. Each category carries some importance. Each element of the game is important for winning, as showcased by the Bucks. A team could shoot the lights out of the gym and still finish in the middle of the standings if it does poorly in other statistical areas.
With an average differential across the league of 4. This is highlighted by all of the top 10 teams in offensive rating to make the playoffs in the season. It was the only category where every single top finisher made the postseason. The Spurs were 18th in points per game, but finished ninth in offensive rating.
Scoring efficiency is important for a strong offensive rating, which is important for winning games. There are some teams which failed despite having good offensive efficiency. The Pelicans and Wizards finished 12th and 15th, respectively, in offensive rating but ended up 22nd and 25th in wins. Others, like the Rockets and Trail Blazers, used high-powered offenses to carry lackluster defensive showings. Defensive efficiency is also critical, but not to the level of its counterpart.
The average differential for defensive efficiency was 5. Seven of the teams that finished top 10 in defensive rating made the playoffs in The Bucks finished 11th in points allowed per game but first in defensive rating.
The Rockets finished 10th in points allowed per game, but 17th in defensive rating. A low defensive rating can be overcome by a strong offense. The Spurs, Clippers, Rockets and Trail Blazers all featured defensive ratings below the league average and made the playoffs.
For data points that are highly influential we should first make sure that the observation is registered correctly. If it turns out to be correctly reported, we should try to remove it from the data and see just how much it influences the regression line and ask the question: Does it make us change the conclusion? We cannot let a single datapoint, or even a few datapoints, change our statistical conclusion. If it does make us change the statistical conclusions, we should try to get more observations near that X value.
If we cannot manage to get more observations at the given X value or close to, and we can see that it does make a change in our statistical conclusion, we can consider to do either two reports: One with the influential point and one without.
This procedure, of not including the influential point in the analysis, can bring in a high risk of mistakes. Sometimes one or more influential points can have such a high influence on the regression line that they bring the line closer to them:. The formula shows that the closer the Xi value falls to the mean of X, the lower the leverage. Discrete vs. Binomial distribution Poisson distribution Geometric distribution Hypergeometric dist.
Continuous vs. Normal distribution Empirical Rule Z-table for proportions Student's t-distribution. Statistical questions Census and sampling Non-probability sampling Probability sampling Bias. Confidence intervals CI for a population CI for a mean. Hypothesis testing One-tailed tests Two-tailed tests Test around 1 proportion Hypoth.
Scatter plots Correlation coefficient Regression line Squared errors of line Coef. Comparing 2 proportions Comparing 2 means Pooled variance t-proced. You must be logged in to post a comment. Email Address. Learning statistics. Doing statistics.
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