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Aim The aim was to predict and understand variations in swimmer performance between individual and relay events, and develop a predictive model for the 4x200-m swimming freestyle relay event to help inform team selection and strategy. Data and methods Race data for 716 relay finals (4 x 200-m freestyle) from 14 international competitions between 2010–2018 were analysed. Individual 200-m freestyle season best time for the same year was located for each swimmer. Linear regression and machine learning was applied to 4 x 200-m swimming freestyle relay events. Results Compared to the individual event, the lowest ranked swimmer in the team (-0.62 s, CI = [−0.94, −0.30]) and American swimmers (−0.48 s [−0.89, −0.08]) typically swam faster 200-m times in relay events. Random forest models predicted gold, silver, bronze and non-medal with 100%, up to 41%, up to 63%, and 93% sensitivity, respectively. Discussion Team finishing position was strongly associated with the differential time to the fastest team (mean decrease in Gini (MDG) when this variable was omitted = 31.3), world rankings of team members (average ranking MDG of 18.9), and the order of swimmers (MDG = 6.9). Differential times are based on the sum of individual swimmer’s season’s best times, and along with world rankings, reflect team strength. In contrast, the order of swimmers reflects strategy. This type of analysis could assist coaches and support staff in selecting swimmers and team orders for relay events to enhance the likelihood of success.

X-ray images are broadly used for diagnosis and analysis in medical science. In a number of medical applications, such as disease detection, automatic classification systems are very beneficial. Therefore, finding an appropriate classification technique is helpful. In this paper, it is aimed to compare the performance of different classification methods on categorizing X-ray images. In this regard, we have done our experiments in two phases. In phase 1, the effect of wavelet transformation (WT) as a feature selection method is evaluated. Obtaining almost similar performance (about 78%) for probabilistic neural network (PNN) and WT-based PNN, while reducing computational cost, leads us to apply WT on datasets in phase 2. During phase 2, random forest (RF), decision tree (DT), support vector machine (SVM) and Naive Bayes (NB) are applied on obtained images from WT. The results reveal better performance of RF compared to other methods by 82% accuracy. With very close accuracy, SVM got the next place (81%). DT and NB classifiers are the next ones by about 66% accuracy.

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