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We investigated the impact of sleep and training load of Division -1 women's basketball players on their game performance and injury prediction using machine learning algorithms. The data was collected during a pandemic-condensed season with unpredictable interruptions to the games and athletic training schedules. We collected data from sleep monitoring devices, training data from coaches, injury reports from medical staff, and weekly survey data from athletes for 22 weeks. With proper data imputation, interpretable feature set, data balancing, and classifiers, we showed that we could predict game performance and injuries with more than 90% accuracy. More importantly, our F1 and F2 scores of 0.94 and 0.83 for game performance and injuries, respectively, show that we can use the prediction for informative analysis in the future for coaches to make insightful decisions. Our data analysis also showed that collegiate athletes sleep less than the recommended hours (6-7 instead of 8 hours). This coupled with a long hiatus in games and training increases the risk of injury. Varied training and higher heart rate variability (due to better quality sleep) indicated a better performance, while athletes with poor sleep patterns, were more prone to injuries.

Map-Reduce is a programming model and execution an environment developed by Google to process very large amounts of data. Query optimizer is needed to find more efficient plans for declarative SQL query. In classic database: join algorithms are optimized to execute the entire query result, but they ignore the importance of tables order especially in multi-join query. But we can see that the orders for tables are an important factor to get the best performance of a query plan and will be very effective in performance when join tables content huge number of rows in addition to more than one join operation. In this paper we proposed a new technique called JOMR (Join Order In Map-Reduce) that optimizes and enhances Map-Reduce job. This technique uses enhanced parallel TravelSalesman Problem (TSP) using Map-Reduce for improving the performance of query plans according to change the order for join tables. Also we build a cost model that supports our algorithm to find best join order. We will focus on Hive especially multi-join query and our experiments result for JOMR algorithm proving the effectiveness of our query optimizer and this performance is improved more when increasing the number of join and size of data.

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