Lacrosse Cross-Checking Penalty
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The cross-checking penalty in lacrosse is a personal foul enforced when a referee judges a player’s safety is at risk. A cross-checking foul occurs when an opposing player extends the shaft of their stick into a player running towards them. The call is dependent on the location of impact and the referee’s discretion to be called.
Table of Contents
- Definition
- Result
- Referee Signal
- Examples
- Helpful Links
- Similar Penalties to Cross-Checking
- FAQ
Definition
The official definition of cross-checking varies from league and level of play; according to the official NCAA Men’s Lacrosse rules, the foul occurs when a player hits an opposing player with the middle of their shaft and the player’s hands are placed wide apart on the stick. Conversely, the NFHS and Youth Lacrosse rules define the penalty as a player performing a check by using the middle of their shaft to thrust or extend away from their body, using the area between their hands to hit their opponent.
Regardless of league, the act of extending the cross-section of the shaft into the opponent is a typical dead giveaway to referees to make the call. The penalty is dependent on the referee witnessing the rule being broken firsthand. If the foul is observed, the referee will throw a flag and assess a personal foul to the offending player. This call can result in a one to three-minute penalty for the player who performed the illegal cross-check and even ejection from the remainder of the game, depending on the severity of the offense.
Result
The result for a cross-checking penalty varies by league, but it is typically counted as a personal foul on the offending player. In men’s high school lacrosse, the foul generally comes with a one to three-minute period in the penalty box. The NCAA is stricter with its enforcement and can even lead to ejection and suspension from games. The only formally recognized lacrosse league to allow any form of cross-checking is box lacrosse.
When the referee witnesses a cross-check being performed by a player, they throw a penalty flag and wait for a loose ball or change of possession to stop play. They then signal the penalty by pushing both of their arms straight in and out in a pushing manner with closed fists to imitate the cross-checking motion. The referee will assess the personal foul on the player being penalized and announce the penalty time they must serve, based on the level of the transgression.
Examples
- A defender thrusts their stick illegally into an opposing attacker and hits their head.
- A player uses the shaft of their stick as the initial point of contact while playing defense on another player.
- A player uses the cross-section of their stick to level an opponent in an aggressive or dangerous manner.
- How It Works: Cross-Checking
- Beginner Lacrosse: Lacrosse Penalties
- USA Lacrosse Rules
- NCAA Men’s Lacrosse Rules
- Video: Cross-Checking
Similar Penalties to Cross-Checking
- Illegal Body Check
- Unnecessary Roughness
- Unsportsmanlike Conduct
- Spearing
FAQ
What is cross-checking in lacrosse?
Cross-checking is the act of extending the cross-section of the shaft into an opponent to impede their movement, making contact with your hands spread apart. This penalty is called when the stick is not being used properly by a player and could potentially lead to danger or injury for other opposing players. What does and does not constitute cross-checking is ultimately up to the referee’s discretion and will be called differently at various levels of play.
What are the consequences of being called for cross-checking in lacrosse?
The consequence for a cross-checking penalty varies by league and level of play. In most recognized leagues, the action is classified as a personal foul and comes with a time penalty between one and three minutes for the player who performed the cross-check. Some leagues may even eject the player if the action is violent and dangerous enough; this is dictated by the referee’s judgment.
How are body checking and cross checking different in lacrosse?
Body checking may be similar to cross checking, but it is not the same. Body checking is defined as a player using their body to try and take the ball away from an opponent during play. Like cross checking, it can be considered a foul if performed in a dangerous manner; this is left to the referee’s judgment can be slightly ambiguous. Both rules focus on the point of contact, but body checking is concentrated on shoulder-to-shoulder contact instead of the location of contact the stick makes with the opponent, as seen in cross checking.
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Pages Related to Lacrosse Cross-Checking Penalty
- Lacrosse Illegal Body Check Penalty
- Types Of Lacrosse Sticks
- Lacrosse Penalties
- Lacrosse Holding Penalty
- Lacrosse Face-Off Violation
- Top 10 Lacrosse Brands
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Lloydminster Brutes Lacrosse : Website by RAMP InterActive
The cross-check in the game of box lacrosse is a legal play. Rule 40 of the CLA Rulebook states:
“A legal cross-check shall be defined as a check applied with the portion of the stick held between the hands, on an opponent:
- From the front or side
- Below the shoulders
- Above the waist
- The extension of the arms while the check is being delivered is permissible.
”
The game of Box Lacrosse allows the cross-checking of players with the ball and without the ball. In Pee Wee and younger, the non-ball carrier can only be cross-checked inside the dotted line. In Bantam and older, the non-ball carrier can be cross-checked in the defensive zone.
The purpose of Rule 40 is to provide the guidelines of what is a legal and an illegal cross-check. The game of lacrosse is a physical game and the rules are in place to ensure fairness and player safety.
The cross-check is a skill that is part of playing defense. From a coaching perspective, it is critical that we teach players to play defense first. To know where the ball is, where the opposing player is, and how to prepare to help a teammate. A defender uses the cross-check on the ball carrier to stop the opposing player from getting into the prime scoring areas. A defender uses the cross-check on the non-ball carrier to stop the opposing player from advancing into the prime scoring areas to receive a pass. For example, if a non-ball carrier cuts through the middle of the floor, the defender can cross-check that player to deter their path towards the net.
The “Clear the House” mentality of playing defense needs to stop! Excessive force on the non-ball carrier is illegal and is a penalty. The referee has the discretion on whether a player is defending their zone or using excessive force against an opponent. Players need to use the cross-check as part of their defensive strategy, not in an attempt to hurt or intimidate opposing players.
Coaches can make a significant difference in the game by understanding the purpose of cross-checking, in its function and its implementation in the sport. The game of Box Lacrosse is inherently physical, it is why many people love the sport. The speed and contact make it a great game to play and watch. However, it is important to play the game within the rules, in order for the game to be safe for all participants.
/cloud/ablax/files/Blog%20Posts/Cross-Checking%20in%20the%20Game%20of%20Box%20Lacrosse. pdf
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What is cross-validation | Data Science
Cross-validation ( Cross-validation ) is a method for evaluating an analytical model and its behavior on independent data. When evaluating the model, the available data is divided into k parts. Then, the model is trained on k−1 parts of the data, and the rest of the data is used for testing. The procedure is repeated k times; in the end, each of the k pieces of data is used for testing. The result is an estimate of the effectiveness of the chosen model with the most uniform use of the available data.
Typically cross-validation is used in situations where the goal is prediction and one would like to evaluate how the predictive model is able to perform in practice. One round of cross-validation involves splitting the dataset into parts, then building the model on one part (called the training set), and validating the model on the other part (called the test set). To reduce the scatter of results, different cross-validation rounds are run on different partitions, and the validation results are averaged over all rounds.
Cross-validation is important to guard against data-driven hypotheses (“Type III errors”), especially when obtaining additional data is difficult or impossible.
Suppose we have a model with one or more unknown parameters, and a dataset on which this model can be optimized (training set). The fitting process optimizes the model parameters and makes the model as fit to the training set as possible. If we now take an independent sample of data to validate the model from the same source as we took the training set from, we usually find that the model describes the test data worse than the training set. This is called overfitting, and is especially common in situations where the size of the training set is small, or when the number of parameters in the model is large. Cross-validation is a way to evaluate the ability of a model to work on a hypothetical test set when such a set cannot be obtained explicitly.
Common types of cross-validation
K-fold cross-validation
In this case, the original data set is split into K blocks of the same size. Of the K blocks, one is left to test the model, and the remaining K-1 blocks are used as a training set. The process is repeated K times, and each of the blocks is used once as a test set. K results are obtained, one for each block, and they are averaged or combined in some other way to give one score. The advantage of this method over random subsampling is that all observations are used for both training and testing of the model, and each observation is used for testing exactly once. Cross-validation on 10 blocks is often used, but there are no specific recommendations for choosing the number of blocks.
In layered cross-validation, blocks are selected so that the average of the model response is approximately equal across all blocks.
Random subsampling validation
This method randomly splits the dataset into training and test sets. For each such split, the model is fitted to the training data and the prediction accuracy is evaluated on the test set. The results are then averaged over all partitions. The advantage of this method over cross-validation on K blocks is that the proportions of the training and test sets do not depend on the number of repetitions (blocks). The disadvantage of the method is that some observations may never be included in the test set, while others may be included in it more than once. In other words, test cases may overlap. Also, since the splits are random, the results will be different if the analysis is repeated.
In the layered version of this method, random samples are generated in such a way that the average response of the model is equal across the training and test sets. This is especially useful when the model response is binary, with unequal proportions of responses across the data.
Element-wise cross-validation (Leave-one-out, LOO)
Here, a single observation is used as a test dataset, and the remaining observations from the original dataset are used as a training one. The cycle is repeated until each observation is used once as a test. This is the same as K-box cross-validation, where K is equal to the number of observations in the original dataset.
Model fit assessment
The purpose of cross-validation is to assess the expected level of fit of the model to data independent of the data on which the model was trained. It can be used to evaluate any quantitative measure of fit that is appropriate for the data and model. For example, for a binary classification problem, each case in the test set will be predicted correctly or incorrectly. In this situation, the error rate can be used as a fit score, although other scores can be used. If the predictor is continuously distributed, the standard error, the root of the standard error, or the median absolute deviation can be used to evaluate the fit.
Cross-validation applications
Cross-validation can be used to compare the results of different predictive modeling procedures. For example, suppose that we are interested in optical character recognition, and we are considering using either support vectors (Support Vector Machines, SVM), or k nearest neighbors (k nearest neighbors, KNN). With cross-validation, we could objectively compare the two methods in terms of their relative misclassification rates. If we simply compare these methods by their training set errors, KNN is likely to perform better because it is more flexible and therefore more prone to overfitting than SVM.
Cross-validation can also be used for parameter selection. Suppose we have 20 parameters that we could use in the model. The task is to choose the parameters, the use of which will give a model with the best predictive abilities. If we compare subsets of parameters by their errors on the test set, the best results will be obtained when using all parameters. However, with cross-validation, the model with the best generalizability usually includes only some subset of the parameters that are sufficiently informative.
Computational performance issues
Most forms of cross-validation are fairly easy to implement if there is a ready-made implementation of the prediction method. In particular, the prediction method is needed only in the form of a “black box”, there is no need to get into the details of its implementation. If the prediction method is resource-intensive enough in training, cross-validation can be slow because training is performed many times sequentially. In some cases, such as least squares or kernel regression, cross-validation can be greatly accelerated by precomputing some values that are reused in training, or by using “update rules” such as the Sherman-Morrison formula. However, care must be taken to ensure that the validation dataset is completely separated from the training dataset, otherwise bias may occur. An extreme example of speeding up cross-validation occurs in the case of linear regression, where the results of cross-validation have an explicit analytical form known as PRESS (prediction residual error sum of squares).
Limitations and misuse of cross-validation
Cross-validation only gives meaningful results when the training set and the test data set come from the same source, from the same population. In many applications of predictive models, the structure of the system under study changes over time. This can induce systematic deviations of the training and validation datasets. For example, if a stock price prediction model is trained on data from a particular five-year period, it is unrealistic to consider the subsequent five-year period as a sample from the same population.
If performed correctly and the datasets are from the same population, cross-validation results with little or no bias. However, there are many ways to misuse cross-validation. In this case, the prediction error on the actual validation data set is likely to be much worse than expected from the cross-validation results.
Ways to misuse cross-validation:
1. Use cross-validation on multiple models, and take only the results of the best model.
2. Conduct an initial analysis to determine the most informative set of parameters using the full set of data. If parameter selection is required in a prediction model, it must be performed sequentially on each training set. If cross-validation is used to determine the set of parameters used by the model, internal cross-validation must be performed on each training set to determine the set of parameters.
3. Allowing some training data to also fall into the test set – this can happen due to the existence of duplicate observations in the original set.
Source
crosscheck – from English to Russian
Interpretation
Translation
1
crosscheckcrosscheck double check crosscheck rev. crosscheck crosscheck crosscheck crosscheck check against different sources crosscheck rev.
check using different methods
English-Russian short dictionary > crosscheck
2
crosscheckcrosscheck n
cross check
crosscheck the readings
check readings
English-Russian aviation dictionary > crosscheck
3
crosscheckcrosscheck
Personal Socrates > crosscheck
4
crosscheckcross check
noun:crosscheck (crosscheck)
double check (double check, crosscheck)
verb:
crosscheck (crosscheck)
English-Russian combinatory dictionary > crosscheck
5
crosscheckcross check;
cross validation
cross-checking with different sources;
double check cross-check against different sources check theories, data, etc.using different methods (anthropology) compare human behavior in different eras compare the behavior of two people under the same conditions
crosscheck ~ rev. cross-check ~ cross-check ~ check against different sources ~ rev. check using different methodsLarge English-Russian and Russian-English dictionary of languages > crosscheck
6
crosscheck1. [ʹkrɒstʃek]
n
cross-checking with different sources; double check
2. [͵krɒsʹtʃek]
v
1. 1) cross-check against different sources
2) check theories, data etc. using various methods
2. 1) anthrop. compare the behavior of a person in different eras
2) compare the behavior of two people in the same conditions
NBARS > crosscheck
7
crosscheckcross check; cross check
English-Russian dictionary of technical terms > crosscheck
8
crosscheck[ˌkrɒs’tʃek]
1) General subject: double check, cross-check with different sources, cross-check with different sources, check theories, data, etc.
using different methods, compare the behavior of two people in the same conditions
2) Aviation: cross-check (e.g. instrument readings)
3) Sport: push with a stick (hockey term), wrong attack of an opponent
4) Technique: cross-check, cross-check
5) Anthropology: compare human behavior in different eras
6 ) Taxes: cross check (English quote is from an article in the New York Times)
7) Business vocabulary: double-check, check with different sources, check with different methods
The English-Russian combinatory dictionary > crosscheck
9
crosscheck1. cross check ( eg left and right artificial horizon readings )
2. pl. secondary navigation radio aids
English-Russian dictionary of civil aviation > crosscheck
10
crosscheckThu
cross check || perform cross-checking ( e.
g. correct score )
English-Russian electronics dictionary > crosscheck
eleven
crosscheckThu.
cross check || perform cross-checking ( e.g. correct score )
The New English-Russian Dictionary of Radio-electronics > crosscheck
12
crosscheck1 (n) double check; cross-check with different sources
2 (v) cross-check with different sources; compare the behavior of two people in the same conditions; compare human behavior in different eras
* * *
n. double check
The new English-Russian dictionary of financial markets > crosscheck
13
crosscheck[ˌkrɔsʧek]
ch.
cross-check with different sources
English-Russian modern dictionary > crosscheck
14
crosscheck1.