\[ \text{Most teams that use traditional metrics for assessing player values perform below expectation for the amound of salary they spend} \\ \text{on their teams. Teams that sometimes use sabermetrics generally perform better than the expection. Teams that often or always} \\ \text{use sabermetrics perform at expectation or better.} \]

\[ \text{Teams that fall below the line are obtaining less WAR per-dollar of their budget, while those who are above the line}\\ \text{are earning more "bang for their buck."} \]

\[ \text{The regression line fits Often or Always teams almost as well as it does the general population of teams.} \text{This tells us that WAR of the top players is a helpful predictor for the number of wins. Teams that sometimes use sabermetrics} \\ \text{requently fall above expectation compared to teams with local WAR numbers. We can interpret that overreliance on WAR might lead} \\ \text{to overlooking better player statistics.} \]

\[ \text{Hits by Batters (HBB) is an important traditional statistic. Sabermetrics puts more weight on On Base Percentage (OBP).} \\ \text{We see why OBP is the better statistic for 2016 data when we express as the inputs for runs scored. The} \ r^2 \ \text{of HBB and OBP is} \\ \text{approximately 0.483 and 0.651 respectively.} \]

\[ \text{Teams that use sabermetrics often or always do not appear to have a strong relationship between homeruns and number of wins.} \\ \text{Teams that sometimes use sabermetrics have a clear positive correlation. Teams that rarely or never use sabermetrics} \\ \text{might have a negative correlation.} \]