Exploring elite soccer teams’ performances during different match-status periods of close matches’ comebacks

The aim of the present study was to examine winning and losing teams’ performances during the four different match-status periods that occur in close soccer matches’ comebacks (1° drawing; 2° winning/losing; 3° drawing; and 4° losing/winning). The variables (i.e., shots, passing effectiveness and ball possession) were gathered from 17 matches of the Spanish professional soccer league. Relative-phase analysis of ball possession between teams revealed a shift from anti-phase to in-phase relations from period 1 to 4. Pass efficacy revealed a particular trend of anti-phase relations in period 2 and the analysis of shots revealed similar phase relations between periods. Statistically significant differences were observed between winning and losing teams in Period 3 for ball possession and passing effectiveness. Also, statistically significant differences among periods were observed for winning teams in ball possession with period 4 as the most differentiated from the other periods. Besides, winning teams also showed significant differences between periods in passing effectiveness (period 4 vs 3), and in shots (period 3 vs periods 1, 2 and 4). On the other hand, ball possession showed significant differences for losing teams with periods 3 and 4 different than periods 1 and 2. The current findings can be used when controlling match-status scenarios and key performance indicators along the match

BeppoSAX LECS background subtraction techniques

We present 3 methods for the subtraction of non-cosmic and unresolved cosmic backgrounds observed by the Low-Energy Concentrator Spectrometer (LECS) on-board BeppoSAX. Removal of these backgrounds allows a more accurate modeling of the spectral data from point and small-scale extended sources. At high (>|25| degree) galactic latitudes, subtraction using a standard background spectrum works well. At low galactic latitudes, or in complex regions of the X-ray sky, two alternative methods are presented. The first uses counts obtained from two semi-annuli near the outside of the LECS field of view to estimate the background at the source location. The second method uses ROSAT Position Sensitive Proportional Counter (PSPC) all-sky survey data to estimate the LECS background spectrum for a given pointing position. A comparison of the results from these methods provides an estimate of the systematic uncertainties. For high galactic latitude fields, all 3 methods give 3 sigma confidence uncertainties of <0.9 10^-3 count/s (0.1-10 keV), or <1.5 10^-3 count/s (0.1-2 keV). These correspond to 0.1-2.0 keV fluxes of 0.7-1.8 and 0.5-1.1 10^-13 erg/cm2/s for a power-law spectrum with a photon index of 2 and photoelectric absorption of 3 10^20 and 3 10^21 atom/cm2, respectively. At low galactic latitudes, or in complex regions of the X-ray sky, the uncertainties are a factor ~2.5 higher.Comment: 13 pages. Accepted for publication in A&A

Team performance indicators that predict match outcome and points difference in professional rugby league

Performance indicators allow for the objective quantification of performance, however, limited PI research for professional rugby league exists. Therefore, this paper assessed 24 relative PIs (home value minus away) from all 27 rounds of the 2012, 2013 and 2014 European Super League seasons, collected by Opta, amounting to 567 matches. Backwards logistic (match outcome) and linear (points difference) regression models were used alongside exhaustive Chi-Square Automatic Interaction Detection decision trees to identify performance indicators (PIs) and key performance indicators. Teams had a higher chance of winning and would gain more points when they scored first (OR = 1.6, β = 2.4) and increased completed sets (OR = 1.2, β = 1.2) by one unit. Conversely, teams had a lower chance of winning when they increased scoots (OR = 0.9, β = −0.2). However, some PIs which were thought to be important (as identified by previous literature) were removed from the analysis thus calling into question the appropriateness of stepwise methods. Future research may consider utilising dimension reduction techniques when analysing large data-sets that encompass multiple variables