Some of the core attributes of sports are supposed to be integrity, honesty and sportsmanship. However, there have been numerous cases where individual athletes and teams have thrown games and colluded to manipulate sports competitions. Much of the previous work has focused on proving that some types of competitions are computationally hard to manipulate and thus possibly resistant to manipulation. In contrast, in this paper we focus on detecting patterns of manipulations in sports competitions by coalitions of teams. We show that it is possible to successfully detect certain patterns of manipulations in cups and round robins, two of the most common types of sports competitions. The practical benefit of our approach is a tool that can be used by competition organizers to highlight suspicious play within the competition and so aid in the detection of cheating coalitions.

Modern processors have multiple pipelined functional units and can issue more than one instruction per clock cycle. This places a burden on the compiler to schedule the instructions to take maximum advantage of the underlying hardware. Superblocks - a straight-line sequence of code with a single entry point and multiple possible exit points - are a commonly used scheduling region within compilers. Superblock scheduling is NP-complete, and is done suboptimally in production compilers using a greedy algorithm coupled with a heuristic. The heuristic is usually handcrafted, a potentially time-consuming process. In this paper, we show that supervised machine learning techniques can be used to semiautomate the construction of heuristics for superblock scheduling. In our approach, labeled training data were produced using an optimal superblock scheduler. A decision tree learning algorithm was then used to induce a heuristic from the training data. The automatically constructed decision tree heuristic was compared against the best previously proposed, handcrafted heuristics for superblock scheduling on the SPEC 2000 and MediaBench benchmark suites. On these benchmark suites, the decision tree heuristic reduced the number of superblocks that were not optimally scheduled by up to 38 percent, and led to improved performance on some architectural models and competitive performance on others.