EXPLANATION OF DATA AND
Kentucky Ends SEC Season With 1 Point Loss at Florida
Who would have imagined that not only has the conference race been resolved, one full week before this match up, but it is Kentucky, not Florida that is in steady control. This game means nothing for Florida except pride, and one last chance for the Florida seniors to hand any Kentucky team a loss. For Kentucky, this game is huge. A #1 seed in the upcoming NCAA Tournament is at stake.
The teams battled on relatively even terms during the first half, which ended with a 26 - 25 Florida lead. A slow pace, and vigorous defense ruled the day. After several lead changes in the second half, Kentucky slipped ahead 47 -45 on a 3 point play by Morris with 3:45 to play. They stretched the lead to 4 at 49 - 45. With 15 seconds to play, Florida regained its fragile 1 point lead on two Roberson free throws that followed a Walsh 3 and a Sparks turnover. On Kentucky's last possession, its 71st of the game, Kentucky 3 point shot missed, and Florida won the game by 1 point.
Kentucky outrebounded Florida today by 1, ending the recent string of games in which they had been outrebounded. Both teams got 7 offensive rebounds, but Florida made better use of its second chance possessions, outscoring Kentucky 10 to 6 on second chance points.
Based on Season performance of both teams, as well as their schedule strength for this year, I predicted an 1point Kentucky Loss, 70 - 69.
Kentucky finishes the regular season and SEC season with a record of 23 - 4, 14 - 2 in the SEC. The SEC post season tournament begins on Thursday with first round games. Kentucky will play the first round winner of Arkansas or Tennessee on Friday, March 11, 2005 in Atlanta.
I will post a comparison of Kentucky and its next opponent on Thursday night when the opponent is known. Until that time, the Florida comparison and prediction remains here.
Let's compare the teams as of March 2, 2005
Win-Loss Record 23 - 3 19 - 7
Based on each team's early performance with an adjustment for strength of schedule, I would make the following prediction based on data available 3/5/05 at 1 pm. I will update this prediction from time to time between now and game time as indicated by daily changes in the current Strength of Schedule factor.
UK 69 Florida 70
Click Here To View Graphs
There are four sets of graphs, each set showing data for the current season for all games played and data for last season.
Graph Set 1: Average Offensive and Defensive Efficiency, both in terms of points per possession. The distance between the offensive and defensive data is the average Net Game Efficiency [See Graph Set 2]. A second set of data is also included, the five game running average for offensive and defensive efficiency. By comparing the slope of the average data and the position of the 5 game averages relative to the season average, one can assess the current state of play.
Graph Set 2: Net Game Efficiency after each game of the current and past season. This graph provides important information about the season. Net Game Efficiency values above 0.25 ppp have correlated to NCAA final four quality seasons. Values between 0.10 and 0.25 ppp indicate successful seasons, and probable post season play. Values between -0.10 and 0.10 ppp are indicate of average teams. This graph shows instantly how Kentucky is doing at any time during the season.
Graph Set 3: The Power Rating is the ratio of a team's offensive and defensive efficiencies. Each game has a unique power rating as well. This bar graph shows the distribution of game power ratings. Each bar represents a Power Rating Increment of 0.1, e.g. .8 to .89 is a single bar as is 1.10 to 1.19. Notice for 2003-04 how the data forms a bell shaped distribution around its mean value. The data for each season can be expressed in its statistical terms, a mean, standard deviation, etc. Certainly, a season with an average power rating of 1.25 with a standard deviation of .20 is better than seasons having a mean of 1.10 with a standard deviation of 0.20 or a season having a mean of 125 and a standard deviation of 0.35. Even after three games, a trend is emerging on this graph. It appears that this Kentucky team will be more powerful than its predecessor.
Graph Set 4: Another bar graph of game data. This time it is concurrent plots for offensive [foreground] and defensive [background] efficiencies. Just as the power forms a classic bell shape, so do each of these data sets. The separation of the two sets is important to winning percentage. The greater the separation, the higher the winning percentage. The separation is the Net Game Efficiency. Looking at the difference in this manner provides some insight about why even the very good teams sometimes lose to weaker teams. As this data shows, every team will experience variable performance, offensively and defensively, game to game. These variations can be represented statistically just like the power data can, with means, etc. However, unless the separation between the offensive and defensive data is so large [over the combined standard deviations] that there is no overlap of the two data set, there is always some probability that the better team will lose to the weaker team. The smaller the overlap, the less likely it is that the better team will lose. The greater the overlap, the higher the percentage of games the team will lose. When the data substantially overlap, the team will win and lose about the same number of games.